{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) "
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential, load_model\n",
        "from keras.layers import LSTM, Dense, Dropout\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo_finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-02-10T01:41:25.763Z",
          "iopub.execute_input": "2022-02-10T01:41:25.771Z",
          "iopub.status.idle": "2022-02-10T01:41:25.780Z",
          "shell.execute_reply": "2022-02-10T01:41:25.790Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2022-01-14'\n",
        "\n",
        "# Read data \n",
        "df = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "df.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:22.812Z",
          "iopub.execute_input": "2022-01-16T22:23:22.816Z",
          "iopub.status.idle": "2022-01-16T22:23:24.176Z",
          "shell.execute_reply": "2022-01-16T22:23:24.209Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['Adj Close'])\n",
        "plt.title('Stock Closing Price')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price')"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "Text(0, 0.5, 'Price')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:24.182Z",
          "iopub.execute_input": "2022-01-16T22:23:24.187Z",
          "shell.execute_reply": "2022-01-16T22:23:24.293Z",
          "iopub.status.idle": "2022-01-16T22:23:24.300Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.reset_index()"
      ],
      "outputs": [],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-01-16T22:23:24.451Z",
          "iopub.status.busy": "2022-01-16T22:23:24.307Z",
          "iopub.execute_input": "2022-01-16T22:23:24.312Z",
          "iopub.status.idle": "2022-01-16T22:23:24.320Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df['Adj Close'].values\n",
        "df = df.reshape(-1, 1)"
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-01-16T22:23:24.455Z",
          "iopub.status.busy": "2022-01-16T22:23:24.327Z",
          "iopub.execute_input": "2022-01-16T22:23:24.331Z",
          "iopub.status.idle": "2022-01-16T22:23:24.338Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_train = np.array(df[:int(df.shape[0]*0.8)])\n",
        "dataset_test = np.array(df[int(df.shape[0]*0.8):])"
      ],
      "outputs": [],
      "execution_count": 6,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-01-16T22:23:24.457Z",
          "iopub.status.busy": "2022-01-16T22:23:24.345Z",
          "iopub.execute_input": "2022-01-16T22:23:24.349Z",
          "iopub.status.idle": "2022-01-16T22:23:24.357Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "dataset_train = scaler.fit_transform(dataset_train)\n",
        "dataset_test = scaler.transform(dataset_test)"
      ],
      "outputs": [],
      "execution_count": 7,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-01-16T22:23:24.460Z",
          "iopub.status.busy": "2022-01-16T22:23:24.365Z",
          "iopub.execute_input": "2022-01-16T22:23:24.369Z",
          "iopub.status.idle": "2022-01-16T22:23:24.376Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_dataset(df):\n",
        "    x = []\n",
        "    y = []\n",
        "    for i in range(50, df.shape[0]):\n",
        "        x.append(df[i-50:i, 0])\n",
        "        y.append(df[i, 0])\n",
        "    x = np.array(x)\n",
        "    y = np.array(y)\n",
        "    return x,y"
      ],
      "outputs": [],
      "execution_count": 8,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2022-01-16T22:23:24.464Z",
          "iopub.status.busy": "2022-01-16T22:23:24.383Z",
          "iopub.execute_input": "2022-01-16T22:23:24.387Z",
          "iopub.status.idle": "2022-01-16T22:23:24.394Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = create_dataset(dataset_train)\n",
        "x_test, y_test = create_dataset(dataset_test)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:24.400Z",
          "iopub.execute_input": "2022-01-16T22:23:24.404Z",
          "iopub.status.idle": "2022-01-16T22:23:24.411Z",
          "shell.execute_reply": "2022-01-16T22:23:24.467Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "outputs": [],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:24.417Z",
          "iopub.execute_input": "2022-01-16T22:23:24.421Z",
          "iopub.status.idle": "2022-01-16T22:23:24.428Z",
          "shell.execute_reply": "2022-01-16T22:23:24.470Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=96, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=96,return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=96,return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(LSTM(units=96))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(Dense(units=1))"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From C:\\Users\\Tin Hang\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n"
          ]
        }
      ],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:24.434Z",
          "iopub.execute_input": "2022-01-16T22:23:24.438Z",
          "iopub.status.idle": "2022-01-16T22:23:25.157Z",
          "shell.execute_reply": "2022-01-16T22:23:25.194Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
        "x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))"
      ],
      "outputs": [],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:25.166Z",
          "iopub.execute_input": "2022-01-16T22:23:25.171Z",
          "iopub.status.idle": "2022-01-16T22:23:25.179Z",
          "shell.execute_reply": "2022-01-16T22:23:25.197Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='mean_squared_error', optimizer='adam')"
      ],
      "outputs": [],
      "execution_count": 13,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:25.185Z",
          "iopub.execute_input": "2022-01-16T22:23:25.190Z",
          "iopub.status.idle": "2022-01-16T22:23:25.211Z",
          "shell.execute_reply": "2022-01-16T22:23:25.200Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs=50, batch_size=32)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WARNING:tensorflow:From C:\\Users\\Tin Hang\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/50\n",
            "1569/1569 [==============================] - 4s 3ms/step - loss: 0.0124\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 2/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0031\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 3/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0023\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 4/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0021\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 5/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 6/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 7/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 8/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0022\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 9/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 10/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 11/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0018\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 12/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 13/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0015\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 14/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0015\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 15/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 16/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 17/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 18/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0012\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 19/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 20/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 21/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 22/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0012\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 23/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 24/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 25/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0016\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 26/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 27/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 28/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0013\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 29/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0014\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 30/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 31/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 9.5764e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 32/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 33/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0012\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 34/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 35/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 36/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 37/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 9.6013e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 38/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0012\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 39/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 8.5387e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 40/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0010\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 41/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 8.4445e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 42/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0012\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 43/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 9.9257e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 44/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 45/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 9.7822e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 46/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 9.2458e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 47/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 48/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0017\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 49/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 0.0011\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n",
            "Epoch 50/50\n",
            "1569/1569 [==============================] - 3s 2ms/step - loss: 8.7424e-04\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "<keras.callbacks.callbacks.History at 0x25255c8b4e0>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:23:25.218Z",
          "iopub.execute_input": "2022-01-16T22:23:25.223Z",
          "iopub.status.idle": "2022-01-16T22:26:09.077Z",
          "shell.execute_reply": "2022-01-16T22:26:09.109Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = model.predict(x_test)\n",
        "predictions = scaler.inverse_transform(predictions)\n",
        "y_test_scaled = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(16,8))\n",
        "ax.set_facecolor('black')\n",
        "ax.plot(y_test_scaled, color='red', label='Original price')\n",
        "plt.plot(predictions, color='cyan', label='Predicted price')\n",
        "plt.title('Stock price vs Predict price')\n",
        "plt.legend()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 15,
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x2525efc9be0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1152x576 with 1 Axes>",
            "image/png": 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9wPbxK5xzWwC7AQO992sAVwXrBwD7AWsEx9zsnKtPmWrcuDEPP/wwjz32GA0aNOCpp56qsn3ZsmU1Ou/ZZ59Nnz59Um4fOXIko0ePrtG5a2rZsmXstdde7LTTTkW9roiIiIhIMu3iflZSWrdlHH3Xe/++c65Xwupjgcu894uDfaYF63cDHg3Wj3fOjQXWBz7OX5GjMXjwYH766SdGjhzJrbfeSsuWLfn11195/PHHufHGGxk5ciRLly5l7733Zs8998R7z5VXXsmIESPo1KlTlXlKjz76aI4//ngGDBjA8OHDufnmm1mxYgWtWrXinHPO4amnnqJ+/fq88sornHrqqfTq1YtLL72UKVOmAHDyySczcOBA5syZw9lnn820adNYe+21STW9z6abbsruu+/OJ598Qrt27bjkkkto06YNRx99NKutthpfffUV2267LQsWLKBp06b83//9HxMmTODSSy9lzpw51KtXj8suu4xu3brxwAMP8MYbb7B06VI233xzjj766KI8/yIiIiJSWcL+pHNRUlrX1XRKmNWAYc65i4FFwCne+8+ArsAncftNDNbVytXdu/Njs2a1PU0Vqy1YwMkTJmS177Jlyxg+fDgbbrghAN9//z2PPvooXbt25emnn6ZFixbcf//9LFmyhCOOOIKhQ4fyww8//JW0zpo1i3322Yddd921ynlnz57NxRdfzO23307Xrl2ZO3curVq1Yq+99vorOQSrWT3ggAMYNGgQU6ZM4V//+hdPPPEEd955JwMHDuTII4/kww8/5Lnnnkta/oULF7L66qtz0kkncccdd3DHHXdw2mmnAbB06VLuv/9+AG6//fa/jjnnnHM45JBD2GKLLVi8eDHeez755BN+++037rvvPrz3nHzyyXzxxRcMGTIktydfRERERCSDsKb0Y2BroCGwNLriSAHVNCltALQFNgDWAx53zqVuj5qEc+4o4KgaXr8oFi9ezAEHHABYTeluu+3G6NGjWWONNeja1XLtESNGMHbs2L/6i86fP58JEyYwatQotttuO+rXr0+HDh1Yd93q88R+/fXXDB48+K9ztWrVKmk5Pv300yp9UOfPn8+CBQv44osvuOIK6867ySabsNJKKyU9vl69emyzzTYA7LDDDn8lpMBf6+PNnz+f6dOns8UWWwDWjBngk08+YcSIERx44IGAJbsTJkxQUioiIiIieRfWlA7H+gWuAnwfXXGkgGqalE4EnvbWXvRT59wK7PdmEtA9br9uwbpqvPe3A7cDOOeStzsNZFujmW9hn9JETZs2/etn7z2nnHLKX7WooY8++ihv5VixYgX33HPPX8lhbTnn/vo5/l4y8d5z6KGHsueee+alHCIiIiIiqYQ1peGn6n4oKa2rajolzLPAFgDOudWARsAM4HlgP+dcY+dcb2BV4NM8lLNkbbDBBjz11FN/DXr066+/snDhQgYPHswbb7zB8uXLmTFjBiNHjqx27FprrcWoUaOYNMny9rlz5wLQrFkzFixYUOUajz/++F+Pf/jhBwCGDBnCa6+9BlgS/McffyQt44oVK3j77bcBeO211xg0aFDae2revDkdO3bk3XffBWDJkiUsWrSIDTfckOeff/6vsk2bNo1Zs2alPZeIiIiISE20B5YAnweP1a+07spYU+qcewTYHGjvnJsInAfcDdwdTBOzBDgkqDUd45x7HPgWWAYc571fXqjCl4Ldd9+d33//nYMOOgjvPW3atOGqq65iiy224PPPP2efffahc+fOrLXWWtWObdOmDWeeeSannXbaX8fedNNNDBs2jNNPP5333nuPU089lVNOOYXLL7+c/fffn+XLlzN48GDOOOMMjjjiCM4++2z22Wcf1l57bTp37py0jE2bNmXMmDHcddddtG3blksuuSTjfV1wwQVceuml3HbbbTRo0IBLL72UDTbYgPHjx3PYYYcBljxfeOGFtG3btnZPooiIiIhIgvZYrdcfwO8oKa3LXKoRW4taiCTNd1955RXat2+fbHfJ0aabbsr7779ftOvNmDGDHXbYoWjXExEREZG652mgL7A28A420NEmkZZIammk9776QDvUvPmuiIiIiIhIwYQ1pQA/AP0jLIsUlpLSClDMWlIRERERkXxoB8wMfv4heNwmuuJIASkpFRERERGRkhNfUzo9WGokk7qpZJPSFStWRF0EqSG9diIiIiKSq5WAq4CmgMMS0LCmdG6wbBVBuaTwSjYpHTt27F/TrEj5WLZsGWPHjo26GCIiIiJSZrYCTg6WrbBpQsKaUiWldVvGKWGicv7553P++efTt29f6tUr2dxZ4qxYsYKxY8dy/vnnR10UERERESkzYdPcgcB3wc+qKa0MJZuUzp49m+OPPz7qYoiIiIiISBGEgxitDbwZ/Kya0sqgKkgREREREYlcWFO6NjbSLigprRRKSkVEREREJHJhTemqQI/g57D57h/BUklp3aSkVEREREREIhfWlNYHNg1+DmtKlwN/oqS0rlJSKiIiIiIikWsLTAl+3hJYSqyGFKwJr5LSuklJqYiIiIiIRK4N8AUwD+hErOluSElp3aWkVEREREREItcWS0S/Dh4rKa0cSkpFRERERKQoWgMnAC2SbGsDzAJGB49nJGxXUlp3lew8pSIiIiIiUndsCjwIdAcWArfFbauHJayzganBumRJaa+CllCioppSEREREREpqO2Ad4BF2OBFgxK2tw6W8TWlar5bOZSUioiIiIhIQW0PLAAGAyOpnpSG08HMxvqUrgCmJeyjpLTuUlIqIiIiIiIFtRrwIzAf+BJYm6qJSJiUzsJG390ZuCnhHHOBZpR2/8NGURegTCkpFRERERGRgloN+Cn4+UssuVw1bnubYDkrWL5CbM7S0NxgWaq1pe2BOcDeEZejHCkpFRERERGRgmmIDVD0Y/D4y2A5KG6f+Oa7qZR6UtoDaAqcHXVBypCSUhERERERKZjeWJPbMCn9DlhC1aQ0saY0mVJPSsN7WBvYKsqClCElpSIiIiIiUjCrBcuw+e5SYAwwMG6fulBTGialS4CToyxIGVJSKiIiIiIiBRP2Hf0xbt2XVG++Ow9YluY85ZKU3gXsAAyIsCzlRkmpiIiIiIgUzGrADKrWgn4JrAx0Ch63IX3TXSifpPRSbPqbYyMsS7lRUioiIiIiIgUTTgcT78tgGTbhbUvdSEqXABOAUaimNBdKSkVEREREpGBWJdafNPRVsBwULNuQvj8pwB/BspST0vAeJgLdIixLuVFSKiIiIiIiedMQGAcchs1H2p3qNaVzgfHAkOBxNjWly4D5lG5S2pqqSWn36IpSdpSUioiIiIhI3qyMTQPzX2CNYF1iUgrwGbBe8HM2NaVgtaWlmpTG38MEbM7Stql3lzhKSkVEREREJG/CwYu6YIP+QPXmu2BJaR+gPdnVlILVsJZDUjoxWKoJb3aUlIqIiIiISN6ESel0YKvg57FJ9vssWA4DmlA3k1I14c2OklIREREREcmbMCk9K1hOxPqCJhoJrAC2Cx5n03y3XJLSCcFSNaXZUVIqIiIiIiJ50zlY3g98hCWfyfwJfAdsHzwu55pShw10NCd4PAUbmElJaXYaRF0AERERERGpOzphNYaLgW0An2bfz4BDg5/LOSltCdQnVlO6AvgdNd/NlmpKRUREREQkbzoBU4OfFwKL0uz7adzPuTbf3RZYJ+fSFUabYBl/DxNQTWm2lJSKiIiIiEjedCaWlGbyWdzP2daUNg/iceC83IpWMK2DZXxSqrlKs6ekVEREREREkuqFzTuai05Yn8psjAaWBD9nW1MKsC9WY9oxt6IVTE1qSrcDDi5YicqLklIREREREUnqQeDFHI+Jb76byRLgS2xQoHlZ7B8mpUcHyw45laxwkiWlE4FmcdsSHQ+cX8AylRMlpSIiIiIiklQ7YAg2l2g2GmNNWbNNSgHeIvk8psmESen6wbLUk1JI3YS3I7HpcyqdklIREREREUmqWbA8Icv9wyQr2+a7AOcC62W579y4n1/FRr1tnMO1CiVMSufErcs0V2lH7PltWaAylRMlpSIiIiIiklQzYCmwO9A7i/3DpDSXmtJl2Jyl2QiT0gnAs8HP7XO4VqG0oXoT5LCmNF1SCrF5XSuZklIREREREUmqGfAEsBz4Zxb71yQpzcWcYPkYMC34uRSa8Lahai0pWG3xcpI3312JWA2vklIlpSIiIiIikoTDktKfgCeBw7I4JkywCpWUjgPOAK4CpgfrSiUpTRw9eDkwmeQ1pfGjBispVVIqIiIiIiJJNAmW84FR2ABGzVLubQpdUwpwWXD+Uk9KwZrwZkpKNdiRklIREREREUkiTEAXEGua2jrDMZ2w5GxJhv3yIUxKS6FPaWtSJ6XJmu/GJ9KqKVVSKiIiIiIiScQnpeEAQ60yHJPLHKW1NRtrIlvKNaW/AT2onnSFNaVLUVIKSkpFRERERCSJbJPS3YAzg587k9t0MLXhgZmUdlL6LdCU6iMXh0npDygpBSWlIiIiIiKSRLbNd88B/ovVCBazphSsCW+pJKVzkqz/OliulbC+Y7D/rygpBSWlIiIiIiKSRDY1pZ2AdbCk4u9UZlLaHGhI6ppSgDUT1nfEprSZggY6AiWlIiIiIiKSRDZJ6fbBchxwJFaTWuykNOqBjtoEy2RJ6XzgZ5LXlE7DnqtO2PQ7lUxJqYiIiIiIVJNN890dsbk4zwS6BuuK1acUSqOmNF1SCtaEN1VSOgWrZW1bmKKVDSWlIiIiIiJSTXxSuhAbKTa+prQ+sC3wMvAMNugQFLemdAaW0EWZ1GRKSr8BVgUax62LT0pB/UqVlIqIiIiISDXxSSlYE974pHRDrOb0ZWxe0geD9cVuvlsPaJeHc60EvAr0zPG41sEyXU1pA6B/8Dgs73SUlIYaRF0AEREREREpPYlJ6RyqNt/dEas9fTN4fCWWcH1VhLKFpgfLDnE/19QAYDtgY2xU3GxlU1MKNtjRV1jNbn1UUxpPSamIiIiIiFSTqaZ0B+BDYF7weBLw7+IU7S9hIpqPwY5aBstc+3d2C5YzUmz/EatJDvuVhnOUxiellT4Cr5rvioiIiIhINc2AFcDi4HFiUro68FmxC5Ugvqa0tloEyzZp96puY2AM8GeK7cuA70ielM7D+utWek2pklIREREREammGbFaUqjafLcVNnBPMUfaTSasncxHUlqTmtJ6WFL6QYb9viE2V2l8Ugr2HCopFRERERERSZCYlMbXlCYmVlHJlJRuDGyW5blqkpQOxAZIypSUfg30wJ4/JaXVKSkVEREREZFq0iWlYR/IYo60m8xSrAY3MSltCFyB9Xm9K8tz1aT57rBg+X6G/UYHyw2wpHQ5MCtYp6RUAx2JiIiIiEgSzanefLcVVqsVJqVR15SC9StNHOjoRWwO1cnEaiYzqUlN6TDgF2Bihv3exsp5LPA7VsO7Itg2Bdgkh2vWRaopFRERERGRapLVlIIlb2GiF3VNKViyF19T2gDYGrgGuBkrb8MszlOTpHRTMjfdBRss6jZgF6y2ND6Zn4rNW1rJtYVKSkVEREREpJpUSWkrrKZ0BamnQSmmGVRNSjtgSc4PxMrXLovzhElppua7/bHnYDUsOc/UdDd0M9ZsdxBV51SdgpU3H4M1lSslpSIiIiIiUk0zYH7c4znBsjWWjM3EkqyoJdaUhv0zp2BlhOzmMQ37lKarKW0IjAS+Bc4I1mVTUwrWbPeJ4Of4mtKwb2mu86PWJUpKRURERESkmkw1paXQdBcswesAuOBxfFJak5rSRti9J9Mm2NYGODS49g85lPW6YBlfUzo77tyVqpKbLouIiIiISArpktKOlMYgRwCTsBrMDliZ4pPSlYKfs6kpbRn3c1uq3nsoTByPA7qR+zytnwKnAm/GrVNSqqRURERERESSSExK5wTL1lhN6edFLk8q4ci33aialE7FpoyB7GpKW2D9ZOthSWmyEXVbB8spwD01KCvAVQmPw6S0NZVLzXdFRERERKSaTM13S6WmND4pBSvbHGARufUpbYlNIQOpay1bB8vZKbbXhGpKlZSKiIiIiEgSqZLSzliz2FLpU5qYlHYm1qx2MfAn2fcp/TX4OdWgQ2HiOCe3IqYVPq9KSkVERERERAINsH6a8Unp0uDxqsHjUqkpnQYsIXlSCjbYUbZJ6W/Bz6mS0tbBMp81pSuwxFRJqYiIiIiISCAcfTZxsJ+52PycUDo1pR4b7ChVUjqTzM13GwBNiCWlqRLEQtSUgiW5SkpFREREREQC6ZLSsKa0VJJSsCa8takpDeco/R2rdU1XU7oIaxacT7PRQEciIiIiIiJ/SZWUziGWwJVK812wpLQ70BQbiCnXmtJwOph5pK+1bE1+m+6G5qS5ZiVQUioiIiIiIlWkqykNlVpS2o2q08GEZpJ9TemfwCzSD3Q0p2ZFTEvNd0VEREREROJkSkrnAQuLV5yMJmJ9QtcIHic2320D1E9zfHxNabqktDWFqSlVUioiIiIiIhInXfNdKK3+pBCbFmbdYJnYfBdSJ5qQffPdQtaUti7AecuFklIREREREakiU01pOSWlM4Jluia8YVKaqfluawqXlDbHpuGpREpKRURERESkikxJaSn1J4WqSekKYHrctrCmNN1gR2Gf0qia784JlpXahDdjUuqcu9s5N805902SbSc757xzrn3w2DnnrnfOjXXOjXbODSlEoUVEREREpHDKrfnuFGAZ0AlLSJfHbQuT0mxqSsPmu61I3ge1NYWrKQUlpencC2yfuNI51x3YltgcswA7YFMXrQocBdxS+yKKiIiIiEgxlVtN6QpsjlGo2nQXYs1309WUJg50BNX7eLYAGlC4gY6SXbNSZExKvffvE3tt4l0DnAb4uHW7Afd78wnQ2jm3cl5KKiIiIiIiRVFufUoh1oQ3MSnNpqa0BVa7upBY4pNYaxk+nlPD8qWjmtIacM7tBkzy3n+VsKkrMCHu8cRgXbJzHOWc+9w593lNyiAiIiIiIoURJqWJ077MCZalVlMKqZPSBdh9ZKop/TP4OUwQE/uVtg6Wc2pWvLTCc1ZqUtog1wOcc82AM7GmuzXmvb8duD04p8+wu4iIiIiIFEkzYDFV+2YCfA7cDLxV9BJlliopBastzdSndF7wc1hTmiopLWTzXSWl2VsF6A185ZwD6AZ84ZxbH5gEdI/bt1uwTkREREREykQzYH6S9QuB44pclmylS0pnkLn5bmJSGkXz3dYFOHc5yLn5rvf+a+99R+99L+99L+z1H+K9nwI8DxwcjMK7ATDXe/97uvOJiIiIiEhpaUb1/qSlLlNNab6a7xaipnQp9iVApdaUZjMlzCPAx0A/59xE59zhaXZ/GRgHjAXuAP6Rl1KKiIiIiEjRlGNSOhpL7sYk2ZZL891UTWkLWVMaXrdSk9KMzXe99/tn2N4r7mdP6dboi4iIiIhIFsoxKf0eWAlYlGTbDGI1pe2wJrrxg9q0BH4Nfl6OjTKcmMS2DpZzKYw5VG5SWqPRd0VEREREpO5qTvklpZA8IQWrKW0LnIKNHHxQwvYWxJrvAnwD/C1YH2qNJaQr8lHQJGajPqUiIiIiIiJAedaUpjMDS3yuDJZrJ2yPb74LcDI2Yuu5cevaULimu1DZzXeVlIqIiIiISBV1LSn9JVieD/wE9EjYnpiUjsDmrjwRWCNY15rCDHIUUlIqIiIiIiISqGtJ6fNAF+ACrO9o/ByW9bD7nZdwzBlYzej1weNC15TOQUmpiIiIiIhUsH7AfViCVteSUoBwnsoJVE1Kw36jf1bdnVnAtcCWQEespnROwUpnNaWtyC5BGwScBRxawPIUU8bRd0VEREREpO7bFjgYGEndTEpDE4CVgfrYSLstg/WJNaUArwAXYc9NawrffBcsMU13nXeBzYKfZwL3Fq5IRaOaUhERERER+WvKlJOx2sO6nJTWx5rzQvqkdBQ2Wu92FGegI0jfhLcesAnwMHA1Nm1N0wKWqViUlIqIiIiIyF9JaQ+gCXU7KYVYE95UzXfB5jJ9HUtKW1KcmtJ0SWlHLKH+AEuYoWpT5HKlpFRERERERGgP/ACMDh7X9aQ0HIE3XU0pwGtAh+DnOQUqU/y5W6fZJ6zdnUz1+yhnSkpFRERERIR2wHTg8uBxXU9KwxrGTEnp63E/F6OmdF1gL6BPkn2SJaV1oaZUAx2JiIiIiAjtgfHAY8CqwAvRFqdg5mG1konNd1MlpdOwprKDKWxN6YxgeVmwfAXYMWGf+KR0BrACJaUiIiIiIlJHtAc+x0akvSDishRa/LQwYU1psj6lodcofFI6Ffgb4IAjgb5J9umKvT5T45Z1ofmuklIREREREaE9sdq6ui5ZUpqqphTgQWB74LtCFgp4KliuA2yO9bVcEbe9C7GEFKrPuVqu1KdURERERKTCtQAaU1lJaVjDuA4wBZifZv8xWE3pzAKXKzQeaESsuW6oC9Z0N/QbSkpFRERERKQOCKeDqaSktANWS7oD8GK0xalmfLDsnbA+MSmNT67LmZJSEREREZEKV2lJ6W/Bcn+gFaU3qFOYlPZKWJ8sKW1O+rlNy4GSUhERERGRCldpSWk4ncpxwCLgzQjLksxvWF/S+JrShkBHqjffhfJvwqukVERERESkwlVqUro28BalNyfrEiz5jE9KOwfLxJpSKP8mvEpKRUREREQqXKUlpRPjfi61pruh8VRNSuPnKA2FSalqSkVEREREpKy1A5YBc6MuSJEsBqYFP5faIEeh8VTtUxompZPi1k3BalWVlIqIiIiISFlrj0134qMuSBGNAz6napJXSsYD3YAGweOuwTK+ptRj5S/35rsNMu8iIiIiIiJ1WXsqp+lu6BCsdrhU/QLUxxLOcVhN6RKqz5U6AdWUioiIiIhImavEpPRHLNkrVYlzlXYBfqd6bfZvKCkVEREREZEyV4lJaalLnKs0cY7S0ASsmW85J3blXHYREREREcmDsE+plI5JwFKq1pSmSkobAp2KVK5CUFIqIiIiIlLhVFNaepZjCWempHQ81v+0dVFKVRga6EhEREREpIK1wpICJaWlJ5yrtCnQhuRJ6atUnc+0HKmmVERERESkgrUPlkpKS894YHXgwuBxqU5fU1tKSkVEREREKpiS0tL1IbAScAqwEPgq2uIUjJJSEREREZEK1i5YKiktPfdhgxjVB5oDo6MtTsGoT6mIiIiISAVTTWlpWxF1AYpANaUiIiIiIhVMSalETUmpiIiIiEgFaw8sAeZFXRCpWEpKRUREREQqmOYolagpKRURERERqWArA9OiLoRUNCWlIiIiIiIVbG1gTNSFkIqmpFREREREpEK1BXoAX0ZcDqlsSkpFRERERCrUwGD5ZZSFkIqnpFREREREpEKFSelXkZZCKp2SUhERERGRCjUImARMj7gcUtmUlIqIiIiIVKhBqJZUoqekVERERESkAjUCBqD+pBI9JaUiIiIiIhVoANAQJaUSPSWlIiIiIiIVSCPvSqlQUioiIiIiUoEGAfOBnyMuh4iSUhERERGRCjQIGA2siLgcIkpKRUREREQqjEMj70rpUFIqIiIiIlJh+gGtgRERl0MElJSKiIiIiFScDYPlx5GWQsQoKRURERERqTAbATOBH6MuiAhKSkVEREREKs5GWC2pj7ogIigpFRERERGpKG2AAcDwqAsiElBSKiIiIiJSQYYGS/UnlVKhpFREREREpIJsBCwDPou6ICIBJaUiIiIiIhVkI2x+0vlRF0QkoKRURERERKRC1Mea76o/qZQSJaUiIiIiIhViTaAF6k8qpUVJqYiIiIhIhVg1WI6JtBQiVSkpFRERERGpEN2C5YRISyFSlZJSEREREZEK0Q1YAMyOuiAicZSUioiIiIhUiK7AxKgLIZJASamIiIiISIXoBkyKuhAiCZSUioiIiIhUiG6oplRKj5JSEREREZEK4FDzXSlNSkpFRERERCpAB6AhSkql9CgpFRERERGpIxql2RZOB6M+pVJqlJSKiIiIiNQB1wEzgPVSbA+TUtWUSqlRUioiIiIiUuYuA/6Nfbh/Dus7mkhJqZQqJaUiIiIiImXseOA/wM3AUKAFlpg2TdivK7AUmFbU0olkpqRURERERKSMHQO8D/wTGAPsD6wDnJqwXzdgMuCLWjqRzJSUioiIiIiUqVZAf+B1YsnmS1hN6fFYrWlIc5RKqVJSKiIiIiJSpsJBjUYkrL8YaAscG7dOc5RKqVJSKiIiIiJSptYPlp8lrP8Mqz09mVjfUtWUSqlSUioiIiIiUqaGAt8Dc5NsuwjoBBwJtAaaozlKpTQpKRURERERKVNDqd50N/QB8A5wDrBWsE41pVKKMialzrm7nXPTnHPfxK270jn3vXNutHPuGedc67htZzjnxjrnfnDObVegcouIiIiIVLQeWE3op2n2ORFoA9wYPFZSKqUom5rSe4HtE9a9AazpvV8b+BE4A8A5NwDYD1gjOOZm51z9vJVWREREREQAqyWF1DWlAF8BdwBrB4+VlEopypiUeu/fB2YlrHvde78sePgJ1m8aYDfgUe/9Yu/9eGAssf7XIiIiIiKSJ+sDi4DRGfY7G5gNrAB+L3ShRGogH31KDwNeCX7uCkyI2zYxWCciIiIiInk0FBgFLM2w30zgGOBOYFmGfUWiUKuk1Dl3Fva7/VANjj3KOfe5c+7z2pRBRERERKTSDADWJX3T3XiPA0cXrjgitdKgpgc65w4Fdga28t77YPUkoHvcbt1IMfK09/524PbgXD7ZPiIiIiIiUtVGwIvAHODmaIsikhc1qil1zm0PnAbs6r1fELfpeWA/51xj51xvYFXSDwgmIiIiIiJZWh14E5iOJac/RVsckbzIWFPqnHsE2Bxo75ybCJyHjbbbGHjDOQfwiff+GO/9GOfc48C3WLPe47z3ywtVeBERERGRSrI90BTYBvgt4rKI5IuLtbyNsBBqvisiIiKSk9bAtlhfQakc92Kve5eIyyFSAyO99+sm25CP0XdFREREpMgOBh4DVo66IFJUA7G5R0XqEiWlIiIiImWoc7DsFGkppJgaYqPufhlxOUTyTUmpiIiISBnqmLCUuq8/0AjVlErdo6RUREREpAyFNaQdIi2FFNPAYKmkVOoaJaUiIiIiZUg1pZVnELAI+DHicojkm5JSERERkTIU1pQqKa0cA4FvAM23KHWNklIRERGRMhQmo2q+Wzk08q7UVUpKRURERMpMC6Bp8LNqSivDytgXEF9GXA6RQlBSKiIiIlJm4qeBUU1pZdAgR1KXNYi6ACIiIiKSm7B2dDqqKa2LGgLnAjOxJNQD+wXbRkdVKJECUlIqIiIiUmbCRPQbYL0oCyIFsQFwdpL1o4C5RS6LSDEoKRUREREpM2Hz3W+ALbD+pQujK47kWa9guRHQDFgBTAB+japAIgWmpFRERESkzIQ1pWOCZQfgt4jKIvnXC0tERwJLoi2KSFFooCMRERGRMtMRmA1MCh5rsKO6pRcwGSWkUjmUlIqIiIiUmU7AVGBa8FiDHdUtvYFfoi6ESBEpKRUREREpMx2xhHR63GOpO3qhpFQqi5JSERERkTITJqVhTama79Yd9YHuKCmVyqKkVERERKTMhM135wMLUE1pXdIVG4n0l4jLIVJMSkpFREREykgDoB2xWtLpKCmtS3oHy1+iLIRIkSkpFRERESkj7YPltLilmu/WHb2C5fgoCyFSZEpKRURERMpIp2A5NViqprRu6YXNUToh4nKIFJOSUhEREZEyEiagqimtm3ph888ujbgcIsWkpFRERESkjCRLSlVTWnf0Qv1JpfIoKRUREREpI8ma7zYFWkRTHMmz3igplcqjpFRERESkjHQEFgN/BI81V2nd0QDohpJSqTxKSkVERETKSCdiiShxP6sJb/nrBtRHI+9K5VFSKiIiIlJGOhNrugvWfBdUU1oX9AqWv0RYBpEoKCkVERERKSN9qFqTNjlY9o6gLJI/9YH1g59/ibAcIlFQUioiIiJSJupjtWlj49b9DvwGbBxFgSQvzgVmA5cDc4CJkZZGpPiUlIqIiIiUie5AI+DnhPXvA5sWvziSJ0cDPwD7A/3QHKVSeZSUioiIiJSJVYJlsqR0ZaBvcYuTUhNgS2BVbERZSa0j0AV4CHiUqoNYiVQKJaUiIiIiZSJMSscmrH8/WJZKbemRwFvAj9jUNf8XbXFK2uBgOSrSUohES0mpiIiISJlYBVgETEpY/wNWw1YqSWl/rG/kIcBnwN3AjlEWqISFSemXURZCJGJKSkVERETKRF9s5F2fZFsp9SvtjdXm3g/sBHwFPAEMjbJQJWowMA6YG3VBRCKkpFRERESkTKxC9aa7ofexZLBblufqDtyIDZyUb/HT1vyJ1ZL+DjwFtC/A9crZYNR0V0RJqYiIiEiZWIXqgxyFwn6lw7I8157AccC6tS1UAodNWzMubt00YC+gHXBfsI9AS2wwKCWlUumUlIqIiIiUgU5AC1InpV9j/Tg3y/J8fYLlWrUrVjVdgMZUTUrBmvCehNWanpSwrVKT1IHB8otISyESPSWlIiIiImUg1ci7oRXAJ8AGOZ4v30lpmOyOT7LtFuBJ4FJgULBuZSzR/m+ey1EONPKuiFFSKiIiIlIGUs1RGm8EsCbQPIvzFaqmtHewTKwpDR0FTMcGQWoGPBIcczY2t2klGQxMCUKkkikpFRERESkDqwDLgV/S7DMCqA+sk+FcjljyWIia0hXAbym2zwYOD677Bdbc+CjgO6y/aZs8l6eUaZAjEaOkVERERKQM9MUSvaVp9vksWGaaemVloAnWD7UN2Y/Ym43ewATSl/NV4DagH3AncAdwINARuCaPZSlljYA1UFIqAkpKRURERMpCupF3QzOCfTIlpWFT4OeCZS61pY2AVmm2x08Hk86JwCHAv4LHo4AHgF1yKEs56wU0xGqIRSqdklIRERGRMpBNUgrWhDdTUhr2J61JUnopVsPaJMX23qTuTxpvIdavdFHcupFAW/Jbc1uqwtcgm9dUpK5TUioiIiJS4joDHciuVm0EltR1SbNPH6x/6ldYk+BcktKBQHfgiLh1YT/QJkBXsqspTearYLl2DY8vJ+lGKRapNEpKRURERErchsHykyz2/TRYrp9mnz7E+n1+TW5JaZhMnYY15d0YmAxcAvQMtmVTU5rM18FyYNq9CmcQcFCRrtUbqy3WyLsiSkpFRERESt4GwGKyGxRnFLCE9E14+xBLHL8GVgcaZHHuBkAPLDnuDpwDPI3VkJ4IbBrsV9Pav3lBuaKqKT0Xm0u1GOJfA5FKp6RUREREpMRtiE2fsiSLfRdjzWATk9K/Y3OYQtX+qV9jNZ6rYdPJpNM92Od2rEb2bGywnu2wD5WXBfvVJtkaTXQ1pRsDLUjdXzafsh0QSqQSKCkVERERKWENgXWBj3M45hOs+W44Su4awN3ArUBzoBNVa0oB3sYGHbozzXnDprvjgLOA6cDewOvYFC9tgQXA1BzKmugrLEEuRmIYry82JQ1A+yJcL9sBoUQqgZJSERERkRI2EGhKbknpXVjy+e/g8cnBcmNifSbDhOhb4BHgXWAs1lQ4lfik9E0suX0rWHcxMJ/a1/6Nxmpj16jleXK1cdzPHQp8rbbYFwZKSkWMklIRERGREhYOcpRLUvoV8CzWz7MfcCCWqM7EkkeIJUTLgQOA/YAXiSWeyfTGmhBPCh77uG1TgcOA/+ZQzlRlh+L3Ky1mUqqRd0WqUlIqIiIiUsI2ACYSSwSz9V9sqpbXsZrHS4AbgXbB9mTzY47HamU7pzhnH+BXYEWK7Y8Dj+VYzkTjgD8pfr/SjbCaYih8Uto7WKqmVMQoKRUREREpYRuSWy1p6Aus5rMH8AyWAN2ITUMyB5id5JgwSeqdZBsUZ8RYj/VzLWZNaRusufCzweN8JKVbAC7FNtWUilSlpFRERESkRHXCEsSaJKUA52HNai8NHs/AakyfSLF/mHCmasJbrMF5whF4VyG3QYd6AuvksH842nDYRPolYBm1T0q3wgaOOiDF9t7ANKwProgoKRUREREpWesHyxE1PP4LrCnuF3HrLgKOSrH/r8EyWU3pSliCWIzavZHYYEBjsRF+j8zimG7AR8DLWV6jD/AH8BCwK7AUe55nUPukdL1geUSaa6vprkhMNvMki4iIiEgEwhFov067V/4sxvqvJqspLWY/yPuwGt6VgKOx+U+fAmYl7Lcp1jd2HpaMdg3W9wN+yHCNvYBmwN+weVo/xZo2T6f2SengYLk5NtXM2ITtfYLriYhRTamIiIhIiVoD+A1LuoplPOmT0mLUlC4BngceBI7Bpk85L2GfQ4D3sCR6ItAf+GewbWMy2w2rkR2MJbR3BOunU/t5SodgtbbLsBGJ49XH+vmqplQkRkmpiIiISIlaAxhT5GuOI3nz3fg5SotpDHAb8A8s8QRoidWefgocjyWVBwI3Yc1vN8lwzg5YP9LnsHladwLuDLbVtqZ0Jax29GWsj+qhxPqugjUzboiSUpF4SkpFRERESlA9LAmLIinthjVpjdcHG7F3TpHLA1ZL+ic2QFM/4Gysr+xxwPXYHKvh4E3DyVxTuhP2/D6fZFttk9JwKpsvsER3ZWCPuO2rBEuNvCsSoz6lIiIiIiWoDzZnaLGT0vFYwtYT+ClufW+iS6RmYH0/H8aa3DYE7gY+T7Lvh9jARR2wBDOZ3bBm0V8l2TYdG2SpAdb8NldDguWooNw/YoMp9Qa+A27F+q4W+3UVKWWqKRUREREpQeEgR98W+bqp5ipdlWibnL4FDMJGyJ0LnJliv4+CZara0ibAtiSvJYVYItsu9yIC1kf1d2ygpuXARsALwBXBcmZQtmk1PL9IXaSkVERERKQEDQiWUSWl8YMd7YwlpW8XuSyJfsfmAO2OJX3JjMRGEU6VlG6Njbr7XIrtYVJa0ya8g6k6Bc9MrJb3QOBUYF2sFlVEYtR8V0RERKQErYHNG/pnka87BVhELCltCFyNNT29I9VBRbY4w7bPSD3Y0d+wfrHvpdg+I1jWJCltgn2ZkKwW9uEanE+kUqimVERERKQERTHyLoDH+o6GzXePA1YDTqJmfSyj8BHWt7NJwvom2KBDTwFLUxxbm5rSNbEany8y7SgiVSgpFRERESkxUY28GxqHTWuyCzby7SvAqxGVpSY+xEYPXi9h/Y7YlC2PpDk2TEprMlfp4GCp5rkiuVFSKiIiIlJiVsFq9YrdnzQ0HhtU6HlgFjYXaDkZHiwTm/DujzVPfifNsTODZU1qSjfBmv/+UoNjRSqZklIRERGREhOOvBtVTenzwPvA/2FNd39Kv3vJmYX1gY0f7KglNmDTE8CKNMcuxxLTXJPSelhN7Cs5HiciGuhIREREpORENfJu6I0gytmH2KBGDusnuztW+5yu6W5oOtWT0s7YvLGp5modijX5fbEGZRWpdKopFRERESkxqwITgflRF6SMfQS0IZbgH4Q1q/04i2OTJaX3YrWvB6U4ZidsIKjXciyniCgpFRERESk53YAJUReizH0YLDfGmiBvC9yd5bGJSWk9YEOs2e8DwEVJjtkZ+ACYW5PCilQ4JaUiIiIiJaYrMCnqQpS5n4Gp2OBDxwFLgNuzPDYxKe2Pjdr7T6zG9KxgXag7MBB4qVYlFqlcSkpFRERESkw3rPmu1M6HwBbAocBjWJKajRlAO6w/KsD6wfIj4MLg5y3j9t8xWKo/qUjNKCkVERERKSEtg1BNae19hCX4KwE35HDcdGw00NbB46HAHOBHbKCjX6ialO4CjAV+qE1hRSqYklIRERGREtItWKqmtPY+CpYjgM9yOG56sAyb8K4fHO+Dx+8Am2M1qe2AbYBna1FOkUqnpFRERESkhCgpzZ8vgFeBc3I8LpyXdQtsGpm1gU/jtr+NJaNrA/sBjYD7alVSkcqmeUpFRERESkjXYKnmu7W3DNihBsd9hiWhp2BzxTbAaltD7wTLLbGkdBTwTc2LKVLxVFMqIiIiUkLCmtLJkZZCLgf6ApcEj+Ob/07C+o8eiTXtvb+4RROpczImpc65u51z05xz38Sta+uce8M591OwbBOsd865651zY51zo51zQwpZeBEREZG6phswDVgcdUEq3LPYwEabAL8BUxK2vwOsjtXGPlzUkonUPdnUlN4LbJ+w7nTgLe/9qsBbwWOwFhKrBnEUcEt+iikiIiJSGTRHaWlYAVwV/Dwiyfa3g+Wr2JcIIlJzGZNS7/37wKyE1bsR6899H7B73Pr7vfkEaO2cWzlPZRURERGp8zRHaem4H/gYeCrJtjeBccD1RS2RSN1U04GOOnnvfw9+ngJ0Cn7uCkyI229isO53RERERCSjbsAnURdCAGtCvVGKbbOBVYpYFpG6rNaj73rvvXPOZ96zKufcUVgTXxEREREBGgPtUU2piFSWmo6+OzVslhssw6b0k4Ducft1I0W3CO/97d77db3369awDCIiIiJ1SjgdjJJSEakkNU1KnwcOCX4+BHgubv3BwSi8GwBz45r5ioiIiEgamqNURCpRxua7zrlHgM2B9s65icB5wGXA4865w4FfgX2C3V8GdgTGAguAvxegzCIiIiJ1UjhHqWpKRaSSZExKvff7p9i0VZJ9PXBcbQslIiIiUonCpFQ1pSJSSWrafFdERERE8qwr8AcwL+qCiIgUkZJSERERkRKhOUpFpBIpKRUREREpESmnLRARqcOUlIqIiIiUiC7A5KgLISJSZEpKRUREREpEe2KTv4uIVAolpSJSNrYAOkRdCBGRAmkGNAVmRF0QEZEiU1IqIlU8DtwJ9Im6IAlaAK8DF0ZdEBGRAmkfLKdHWgoRkeLLOE+piFSOXsDewc+HAB8CC4FfgJODn6MyGHvD2jHCMoiIFFKYlKqmVEQqjWpKReQvmwTL7YDrgYbYh6RjgdOjKlRg3WDZAxgQZUFERAok7J6gpFREKo1qSkXkLxsDc4E3saayoYeA04B7sFrTKKwLzAFaY7Wl30ZUDhGRQlFNqYhUKtWUishfNgaGAysS1p8KLAP+V/QSxawLvA2MBnaIsBwiIoWipFREKpWSUhEBrAZyDeCjJNsmAxcBewCbF69If1kJWA34HHgZa2bcMoJyiIgUUntgOdYqRESkkigpFREANsTeEJIlpQDXAouIZqChIcHyc+AVoBGwVQTlEBEppPbATMBHXRARkSJTUioigDXdXQZ8mmL7Yqzp7LopthdSeM2RWPPiuagJr4jUPe1R012RtFq3jroEUiBKSkUEsKT0C2BBmn0+x2otXRHKMxAb9RcsKR0HzMIS53eBTYtQBhGRYlJSKpLGv/4FU6fChhtGXRIpACWlIkJDYH1SN90NjQRaAX0LXiL4N3Az1pd1XSwhDo0BVkHDh4tI3dIemB51IURKUYMGcOqp0KgR3H8/NG8edYkye/ZZeOUVWGWVqEtSFpSUigiDgGZkTkrDxHCdgpbGdMdGAT4LS0Djk9IfsES6d56u1QwbWXj1PJ1PRErb4cCJQJuoC5JANaUiKey9N3TvDpddBn36wBVXRF2i9NZeG3bbDbbdFr7+Gv7976hLVPKUlIoIA4PlyAz7fYsNdlSMfqXdgGeBR4PH8X1dfwyWq+XhOg2Bp7APqHvm4XwiUto6ArdgX0RNxL74KhVKSqVirLsuDBwIrVol396oEXz5Jdx6KzRsCCefDN99B2eeCddcA//4B2yySVGLnJO//x0WL7bk9O234brrYHV99Z2W9z7ywAaaUygUEcW14OeBd1ns+zH4d4pQpnng/we+AfitE7a1Ae/Bn1TLa9QD/3hwrqXgry+B10KhUBQ2Tsb+5vcC/yb2t9+gBMrVKijXCSVQFoUiaRx+uGfGDM9NN3nWWqvm57n00qqJwJ13Vt9njz1i27/+2pZHHmnbmjTx/PGH55Zbon9OkkWjRp7p0z2PPmqP27Xz/Pmn5557oi9b9PF5ynww6oRUSalCkb+oB/4V8BPB/wH+0SyPexNLNrPZ98bg3NkksDWN1tiHsxPT7DMN/G21vM6Rcdf5FktQo34NFQpFfqMl+M3jHn8H/oPg50Ow94A+JVDOVYKyHFQCZVEoksbzz1syuHChZ/lyz9Ch2R334oue337znHaa5+qr7cP/bbd59tzTc+ut9viUU6oe8/TTnsmTPf/3f55FizzTplkyGm5/8knPhAnRPyfJYq+97J623Ta27tprPUuWeLp1S35MvXqe1VePvuyFDyWlCkVdjHWp+gFmY+xDzfPgXwx+XieL80wFf0eW1zw0OG+/At7XmsE19k6zz4fUvsZ2VBCAfxv8+0V+/TpgiXXrIl9XoaikOAl7P/knsffIQ4JtmwSPty2Bcg4NyrJ9CZRFoUga06Z57rrL06mTZ+lSz0UXZT5myy3tw/4PP8Q++F93XdV9HnvMs2xZLIlr29azeLHnqqvs8YABnrXXrnrM3/9u5xo4MPrnJTFeesmS8Hr1Yut69LCk9H//S37MCSfY/WyxRfTlL2woKVUo6lo48GPALwPfOVh3FfhFWM1AS/CzwT+R4TwdsA9Cx2d53bWC/Q8o4L3tEFxjgzT73AV+ci2usX5wjaODx4+A/6HIr+E1QRkOLvJ1FYpKipuwv7Pl2JdQc8E3C7atHGw7tgTKuXNQlnVLoCwKRbXo06dqE9pPPvF88EHm4z76yBK0xo0tgdxvv+r7NGvm+fJLz5w5ts8xx2ROODt1sn3OOiv65yY+DjrIEuwLL6y+7d57rRlvhw7Vt4VJ+5gxnoYNo7+PwoWSUoWirsXfsA8wHvwpwbqx4F+K2+ci7INYulrNLYJzbJnldeuDXwD+KfAbUZhavqOCMnVNs89pwT4ta3iNu7B+q+Hx12AfVov1+nUMnkcP/vYiXlehqLR4CfsC7xPs7+3WhO3zsS/0oi7noUH5epVAWRSKanHggfahPexLevnlVpvZtGnV/Ro29GywgadFC88OO1RNZNNF9+6WvE6Z4vnmG8/o0ZmPGTHCM3x44e75iCM8m2yS3b5t2ngeeMDu9913rbY3cZ9+/awp8rPPVl2/xRZ23IMPJm/KXLdCSalCUZfCgf8S6xs1HPvAFdZgHhm3Xwcs8bkrzbn+HRzXMYfrv0wsIZ5JrNYhX3EhNvhIvTT77BZcP5vmyYnRCvsgGt8n9fTgfE2L9BpejtVyf4X1Z436d0qhqKvxDfYlWgfsC6DeCdtHg3+2BMp5CvYe1KIEyqJQVIsbbrD+pGGT1B13tA/xm28e26dpU88rr9j6hQttsJ+ff/Y0aJDdNfr1sybC3ntOPTXz/ueea31bO3SwJLh79/zdb+PGlkBmKn+rVp4LLrBa3qVLPWefXbXZbmIcf7zd3z/+EVv36KOemTOtz+yzz3rmzfN07Rr9a16YUFKqUNSl2BX78HIQ+MODn1/AakU7Jex7A/gl4FdKca7bsUGDcrl+A6z29djg2un6fmYTjcDvHywBfw/43zIcs3pw7Zo0I/5ncOyQuHWHUrxainZYLe2D4E8Nrtu+CNdVKCox/sRG8k61/RnwX5dAOS8Dv7AEyqFQJI3PPvO8+Wbs8UorWTPV886zxy1bWg3h8uWeM86wAY1Gj/bsvntu1xk82JK09u0z7ztkiCUSzz1nSd2ff3pWXjn1/qut5jnzzOyax268cSxROeKI1Pu99JLt8/jjnjXXzO4eX3zRkvZtt/V07Gg1ztdcY9t69bLn9b//jf41L0woKVUoyj06Y31E52O1nz9hTWlbBus8NvhP4nE7Bts2TnHe4eDfqmGZ6mH9OjP1W00XrbFBhjyx/p1vgv8ow3GNsJrGC3K8nsNqJkckrN8+KEO6fqz5iAHB/S7HEusNg+vuWuDrKhSVGO2wv6/j0+xzFfYeWsgRxbOJO8FPKIHnTKGoFk2b2iA9iQMbff655+237eeXXrJ99tmneOVyzjNxoiUTL79s17/xxuT7brSRTWfjvfX7zHTu006zfb/+2vPLLzbNS+I+7dpZAnnxxbmVu0MHO6f3nlmzbNm/f2z7W29Z39KoX/fCRMqktB4iUhbWAFoDzwH3AYcDy4F5wJPBPs8mOe7rYLlWkm0OWBP4poZlWhFceyegeRb7bwl0iHvcHRgObAT8GSwBugETMpxrCTAe6JdDeQG2BlYHrk9YPzVYdsrxfLk4E/gKGAQcDXwHjAQWAcMKeF2RStUrWP6SZp+xQDNg5UIXJoP2wIyIyyCS1DrrQMOG8MknVde/9x5ssAEcdxzsuCOcdBI8/njxyuU9bL89DB5s17/zTjjySOjZs+p+220Hb74JM2fC2LFw/PGZz73xxvDDD3ZPPXvCYYdV32fXXaF+fXjiidzKPX06rL46HHwwfP01PPggfP99bPvTT8OAAdC/f27nLXdR15JWYk3pxuB/JjZiqkKRTRyEfePfN8m2dbDfqR4pjp2NjUCZuL5ncM4js7h+qginVNg3w359gv2+xZqqdsBGu50NflOsCd2Pwb7ZDjzyIvgvcizvC+B/J9ZUOIxwFM6jczxfttEea0b9HFZ7E7/tPbKfJ1ahUGQfe2J/14PS7LNNsM+wIpbrYvD3U3Uwtw/Bv1ECz5lC8Vf06GEj455yin1gT2xSu+uutn7ZMmva61y05e3a1ZrF3nlnbF29ep6xY63Gs3372Mi+G2yQ/lzTp9v0N2CjDM+cWX3Qoxdf9Iwbl//76NLFynjGGdkf07q19cON+jXIHGq+W0rxNPYP8IgSKIuifCIcbbZ5DY79gORzcO4UnHPDWpTLgZ+EDSRSD/x2JJ/SIOw7uQD850HMj7t2eH+rkbm5XRj/w/qLZdvsrm9w7vOSbGuANalNti0fEQ4otUaSbRdjCWuxBllSKColTsT+7lqn2Sf8wuzQIpUpbLLvsb7l/w7Wf49NTRX1c6ZQAJ7eva057OLF1sT0p5+q79OmjfUhnTs3v4MM1Sb+9z8bcGj11e3xbrtZsrHXXva4eXPP7Nmehx9OfY5+/eyYww6zx336eL77zp6LcN1KK9lASOFcqvmO4cOteXQ2+x5wgI1avGxZ5mQ7+lBSWiqxMjaqqKd2/fAUlRfXgP+jhsfejNVIho8vxL6V/xH7XazptCphXIsN0PEdsQ9bo6lae/oJ+E+xOUiXBLFD3PZhwXFnBcs9s7juYcG+G+VQzsWkbqUwLXiuCvH6jQruP9m2cF7WzQp0bYWiUuM6Mk/1VB97P7qoCOVx2PvAROwLqpewv/19wM/ABqaL+jlTKAAbwGj5ckvyRoxIXWt3wQWenXeOvrxhdOhgfUc/+cRTv77nvfc848fbz+E+V11lCffgwclrFg87zBKUfv1i61q18rz6qq0/+2zP/vvbzxttVJj7CGune/ZMv99ll9l+n3ziGTQo+uc/cygpLZU4E/sH9DaWJNQvgTIpyiMexb5Jr8mx4Si53bAEdAnWdPZd8vMhaD1iE9Pvg80z+hX2BUw/8N2D6/8n2H9TbH7U+HM0Dcr1ZbDv+llctxk20FKyAZ4Sw4GfBf6hNPuMxloy5OP12h5L+tcCPzC4p2NT7NsqeP7OzdO1FQqFxbPYe1Gm/X6iOLWUh2DvBQcGj+tjX9jNoLAtNRSKnOPnnz1vvBF9OWoS++1nCcZDD9nyhBOqbu/Vy5r5em/Tr1x4YdXtd91lzXcTz1u/vueee+y46dM9kycXrrlsnz52nbPOSr1P+/aeBQvsPtNNQ1NaoaS0FMKBH4+NLPo37B9TbZpNKior3gf/Tg2PDft97kCsj9UmeS5fYu1je6yG4hmsKa4neX/Y+PiMWE3rylle94hg/90z7LdKsN9hafZ5HRuNOB/PxxXB9aYGz8Ei8G3S7P8BNp9i1L9nCkVdii/BP5/Ffq+SuiVDvqIR9iXacKp2OVgN69bgwR9XAs+ZQvHXdCjZjFJbqvHEE3YPc+fadDWJ2/v08Rx6qOf5522/YcNi277/3qaZSXZe5zzXX2/H3HRTYe/ho4/sOj//7DnnnOoJ8Dnn2PawqXJ5hJLSUohtsX86+2AfTpeBP78EyqUoj/iJ9LV86aI19rt3Gvi7wc+kOLX0pwfXnUh2tRXXB/svwfqnZnON+lgy9z3WLzTVfnsH5x6UZp8HsAGj8nHvz2Fzrf4eXPfRDPsfE+w3sAivi0JRKTEbe1/JtN9N2Pti/LpNsdYLe5Of+YvDAZV2TrItnDs5m24LCkXB47bbrAaxefPoy1LTaN/emu2ee276/Zo2tcGKxoyx+UvXXNOSk9NOS3/cLrtYn9pC3kOrVp7DD/e89pqV6fTTY9uaNPFMnep54YXon+vcQklpKcQdWPPBcNTPj8lfrYyi7sefZDcibar4DUtqp4J/sEhlbhJc15Nd09T9g31/yfE6OwfH7Z9mn0ux/qQN0+xzJTb4Uj7u/Xus3/gArBY02eBP8dEOS8avKNJro1DU9WiFvS+cnMW+4YBIYWuGhticoT6IZcSa3NY0rsfeX5ok2eawL64TRwVXKIoeTZrYQED33ht9WWob8f1I08X221tC8vrr1qx3xgxP377Rlz8+Hn7YBjLaZht7fNRRVubNNou+bLmFktJSiMSmgRdg/+hal0DZFKUdK5H9h6tU8RKW2Hrw+xWx7PthyWC/LPbtFZTvgxyv4cDPIfm0N2G8ho34m+48JwfXr+3ATw2o2cApz2EfhLOtJVYoFKljbezvea8s9t0l2HfH4PEhwePdsNYLb2H/rzNNfZUuxmN/41E/LwpF2jj8cPtwvuWW0ZelmPHYY3bfjz/u6dgx+vIkRrNmntGj7QuDzz7z/Pmn59NPoy9X7pEyKa2HFE1XYHLc49eA+sDW0RRHykg4qfvktHul9zXQHFiO/e4Vy6NAW+CHLPb9BZgA/JzjNTzwObB+mn2GAF9kOM/UYNkpx+sn6gM0JLt7jvcQ0A3YrJbXFxHoFSx/zWLfN4GfgBuBlsB/gC+B54CvgF2AD4EHqdnf55pBeV6owbEiRdO9O1x9NXzwAbzzTtSlKa5DD4X11oN99oFp06IuTXULFsAee8Cnn1r5HngADj446lLlVYOoC1BJumD/+EKfBcvVIyiLlJcwKf29Fuf4Jlh+BMyuXXFyNj+HfbcG5tTgGp8CpwCNgcUJ27oD7YFRGc4xJVh2BsbWoAyhfsEy16T0BWAecCBQYR8HRPKuZ7D8JYt9FwKHAh8A72L/l/eP274A2An4DjgX2CrHsuwSLF/M8TiRonEO7r0X6tWDQw4Ba8lYORYuhM8/j7oU6f38M2y3XdSlKBjVlBZJM6A1VWu6lgJ/AG2iKJCUlS7BsjZJ6VfBstQ/FP0I1OQ7yk+x2smBSbYNCZbZ1pR2rsH144VJ6Y85HrcQeBLYB6utEZGa64UlkzOy3H84cDX2fjEOeCJh+3zgOmBLYFCOZdkF+yJ6SqYdRaLyz3/CllvCiSfC+PFRl0YqkJLSIknV/HIW1rRRJJ18Nd/dF7ip9sUpSZ8Gy7AJbxtg0+DnIViz5dEZzhF+YKxt893VsMR6Tg2OvRVLSP8vrizfAtvUskwilaYn2dWSxjsHeAw4HnvPSHQn1prhpBzO2QEYipruSgnr2hUuuQReeQXuuivq0kiFUlJaJF2D5aSE9UpKJRsrY9/Sz6vleR7Hag7qoslBhEnprcB7wO5YUvodVhOZzkxgGfmpKc216W7oU6x/7D+Cx5dgTQnVz1QkNwPJ/e9wMbAfqVuUzMUS0/2I/V/PZDvsw1apt1KRCva//0H9+nDccVGXRCqYktIiCZtfJqspbVfkskj56ULtmu5Wik+B9YAewF7YB8x7gY3I3HQXYAUwnWiTUrDa7DWAk4HDgnW9a1kmkUrSAegLfFyAc1+PfXj6Z5b7D8NaTXxZgLKI1No229jgPpdeqma7EiklpUWSLilVTalksjK1a7pbKT4F+gNnYSPybon13W5LdkkpWBPeTDUgxwOvYKNnJ2qFNbmtTVL6GPbecBXWz/UzlJSK5GKDYFmIpPQX4HVgtyz33wQbYK7Cho2RKDVsCPvvD88+CxddBEOGpN7vhhvgp5/giiuKWkSRREpKi6Qr8Cc2sFE8JaWSjZVRTWk2wn6lRwFPYQOXHIDVUmQ7mu1wYAtS/102x0bf3B74e5LtNR3kKN5C4O7g57OwQaqUlIpkbwPsC6lCjaX5FtasfuUM+7UDBmCj+ooUxbbbWo3nww/DOuvAf/4DI0fC++9Dz55V9z3mGOjXD044ARYnjlsvUlxKSoukC8lrupSUSjZS/f5IVfEfQK8Nlm9ggx5lGuQodCvQBJseIpm/Y3+z44D/Ai0Sttd0OphEFwNHAvcA47EmxU1reU6RSrEh1lx2UYHOH37JtXmG/TYOlh8WqBwiVWy2mdWOzpwJ228PPXpAp07WV3TtteGrr6wGFaB1azjvPHjzTXj55ShLLQIoKS2aVEnFTGyyWE3/IKk0x34/VFOa2VxsPtYRwCc1PMc32AfIYwCXsK0ecCLWFG9/LFE8NWGffthgSeNqeP3QHGxAlRVYUgqxeRdFJLX62IBnhWi6G/oSm+95ywz7bYL1bS/x2Q+lLhg2DF54AcaNs6ldXnvN5hqdNQtuvhkGDYJvvrEa1AcesNF227SBk0+OuuQigOVDUgRdSf4heVawbEvtR1aVuilsHqakNDu7kHmU3UxuBh4GtsZqWkN7AH2wAYg+BR4BTsNqZaZh/UnXxxLSpbUsQ7wwKe0NfJ/H84rURWthX+YVMildgY3uvUWG/TbB+oSrYaQUzPrrw7nnwk47wdixsPXWVlOa6JdfrCb1zDNt/wYN4O67YXS27YhECks1pUWSrvkuqAmvpJZqkCxJ7hdscKDaeApLMo9NWH8iMBZ4Pnh8MvA01oR3Q6AbVkt7QS2vnyg+KRWR9DYMljVtLZGtt4FVsNG+k2kKrIP6k0oBHXEEDB9uiemZZ1of0ilTUu+/fDn8979Wq3r//XaMSIlQTWkRtMb+OSkplZpQTWnxLcH6cp6M/W3OAtpj/cPOwmpJwF6TA4tQnqlY7a+SUpHMNsBG0f6lwNcJ+5VuAdyXZPv6QCOK1J+0ZUt4/nk4+2z46KNiXFGiMGAADBwIkybBJpvAxRfDK6/YlC5//pn9eT75xEKkhKimtAjC6SWUlEpNDA2WSkqL62nsW7ttg8dbBcs3ku9ecL+gpFQkGxtS2Ka7oTFYi4pUTXg3C5bDi1AWdtkFNt/c+g7W00e7Omedday/6Jgx1if0vfcsIX3kEdhtt9wSUpESpXeuIgibX05Ksk1JqaTigCuwJqOPYgPfSPF8DkwHdgweb4MNbDIyovKMp3pS2gZLnjPNqypSKfoAq1Kc2kkPvIsNdpQ4KNo2wOlYv9M5RSgLe+wBS5faCKuHHFKMK0qxrLwyvPsuDB0K55wDa60F22xjfUgPOshed5G6wHsfeWDv7XU2DrHxz3zvJNsaB9vOKIFy5jtuAH8Z+HVKoCzlGNdgvxs3gK9XAuWpxLgf/LTg+f8V/JMRluVG8LMS1h2E/Y4cXALPlUJRCnE++OXguxbpen/D/gb3i1u3A/hF4L8A364Y5WjSxPPnn55bbvF89JFn0iRP8+aRvxaKPMVdd3kWLfL06RN9WRSK2sfnqfJB1ZTmSRdiNaKJwlqMZM0vFwPzqXs1pf2BfwL/wWqcHoi2OGWnGXAUcC/wL2J9GKW4XgY6YNO/9CC6prtgNaVtsBF+Q5sFy2QDrTShOP1dRUqFAw4G3iJ5y6RCeAr4CrgQa+4/KFj3NdbkP8kYqPm3zTbQvDk8/bRN79GlC5x0UjGuLIU2aBAceihcf71N9SJShykpzZOngZ+Bc4HGCdu6YP+YUk3iPYvyT0p7AcPiHocfltcHXiLWH0+ysyOWmN4TdUEq3OvAcuCS4HHUSSlUbcIb/p31TLL/YcCDcfuI1EVNsS+OwP4H9Sb5oEOF4oGzsSbDJ2KfBWZg7+Gzi1WIPfaAOXOsiecnn8Czz8K//w1NmhSrBFIoV19t84xefHHUJREpOCWleVAf+3Z0NjYVxHCq9i9JNR1MqC4kpecBr2Fzw4F9EJ6Ezc/2LjaCbKukR5a+VkDfIl/zb9iIq0UZtVFSmoVN8dIDSwqj/J46MSldGfsgDMmT0rAv7KaFLJRIxP6HDQK2C3AINt/3M0Uuw4vY9DNXYP/v98L6oxdF/fo2yNGLL8b6Fv7vf9C+vfU3lPLUoAHccgtsuSWcdx7MnRt1iUQKTklpHvTBakdPx74pHQKsEbe9K3U/Ke2DfWO9Q/B4U2yAB4Dvg+XqxS5UnlyEfeBIHMiiUJoCO2PfuKvZbvReDpZR1pJC9aQ0rAEdS/Xmu02wwVegagsGkbpmO6xVyTPAAcATwIIIynE61hrqn9iXsUWzySaWgD4Tl4p/8AF88QWccEIxSyL5stJK8NJLcMwxcOmllpyKVAAlpXkwIFh+S+wb2vgmc93JnJS2K0C5iqlXsNwLm0y8K/B+sO67YFmuSen62OuTz9rSR4HDU2zbAatxfiKP15Oaew5rwvtCxOWYE0R8UjoXK1diUro59uXGd9j0GJqQunD6YV/KSfF1xf4ezsS+PGoC3B1RWd7Dvly+s9gXPvZYq0V79dWq66+9FtZYw/qbSnm5/HLYYgv4+9/hzDPBBgQVqfuiHnm3Loy+G7xl+ObB41/BPxb8vHqw7eg0x98OfnIJ3EdNoyH4ZeCXgv8D/D+Ce+4fbK+HjUR4eZJjLwb/9xK4h1RRH/wCqo+uWJtoE5xvHvhuwbodwH8N/mzwz2MjvtYvgftXWHQugTIA/lPw34NvCf478C+C/yf2+9Qxbr/rwc8H/3/BtvVKoOx1MRz2t+rBfwB+lxIoUyXFfsFzPxh7v1ytBMpU1OjTx7NsmeeSS6pva9TIM3my5+WXoy9nGB07ep580vPtt56vvvK8+KJnhx08zkVftlKJLl1spN2bb46+LApFYSLl6LuRJ6R1ISl9EPz4uMf3g58S/Hw69k9z5TTHX4YlbVHfR02jd3CPjwTLiXH3H8ZoLNmKXzcs2H8ulqhFfR/JYkBQRg/+ijydc9O4cz4Gvl/wHEyPW39rCdy7ovRiK/BLwL+P/Z6ciiVCHvy6cfuNxf7eOgfbTiqBstfF6Bs8v8+C/xF7Hy+VLzAqIW7Cvgit2C/wbr7ZEpjOnZNvP+ss+5C15prRl3XTTS1JXrDA88QTnqee8kyYYOX75hvPHntEX8ZSiP/9z7N0qadXr+jLolAUJpSUFjJGYTUW4eMjsA8qq4H/GPyIDMefGuzfrATuJZtYGfwv4AcFj7cIyr8tNo+iB/94wjGPgf8pYd0nxGoZ/lsC95UsDgjKNwv8m3k6Z1izdWOwnIwl8d2wmvX/gu9VAveuKM04nNiXF+uDHxj8vFewfdXg8THB4x/BP1MC5a6LEdbUrY0lqMvBX1QC5aor0Zz0CedX4F8tgXJGEh07ehYu9Nx2W+p92rTx/PGH54EHsjtn/fqFKesmm1ii9f33nrXWiq1v2NBz4IGeMWPsw+BHH3nuvNPzxhue997zPPaY59xzbb+on+9iRPv2Nt/sffdFXxaFonChpLRQUQ/8QqrWooUfCs8NlmdkOEf4IbNbDa4fRexLrJYGrPmtxxKpe4Of/5lwzPlYE9/GweNwwvFDsYT1D/BtS+DeEuOK4PW9E0tM83HOO7BkvDGWMCwBv0kJ3KuifOI88D+AbwC+NVVrQ48PHvcMHt8FfgbW1DTqcte1uBpr3h8mTk+Cn0msK4ci96iHtT76hVhrkmT7tca+BDirBMocSfz3v57lyz2rrpp+vyuvzK7mrUcPz6xZ1qz21FM9K6+cn3K2bu359VfPjz96Vlop+T7163sOP9wzfrxn0iTP8OGed97xfPedfUi8665onuMhQzwPPWTJYjFf0/79o//9UigKF0pKCxVh861DEtb/jvUZ9FgT0HTn2IPYt+1R3082cWlQ3vuDxxdgCWcDrNZ0EZaYxx8T1iisifVB/Qlr0lsveH6Wg7+kBO4tMV4H/xn4I4kl3rU95wjwbwQ/9wI/tATuU1HeMRf8dcHPL4P/Nm7bIWT3PqTIPd4D/1Hc46HBc/2vEihbuUb4P/Ut8B9i/0cbJtlvp2C/TUugzEWPpk09M2ZYE9hM+3bp4lm82HPTTen3e/hha1o7fLh9MFuyxGpY42s2M0XHjp4TTvCMGGHnOegga6q7ZIln3XVrdq/nn2/lOeWU4j7HQ4ZYku69laHQ1+vVyzN/vufRR6P//VIoChtKSgsVu2L/GBMHEnk0WJ/YZDVZbBbsu3kJ3E828UpQ3lHB4/uwwZ3C7Y2SHDMwOGZvYoOv7BS3/RHsw0f7Eri/+JiG1WyuE5R5z1qerx42AM3VJXBviroTX2NNdBtif0c3xG3rg/3uphtsTZF71Aue6+sS1n+AjTGwKfgWJVDOcoutsN/XzYh9Ybtxkv0uA78YfJMSKHPR44gj7MPTsGHZ7X/77dbUt1u35Ns33LBq8tW3r/Vt/OMPO65798zXOOQQS6q893z2WaxJrvdW81rTe3XOErXlyz1nn+1p0iT7Yxs2TH3P6WKLLSwhHT/e88EHnilTbOCofLx27dolr3l9+WV7vrt2jf73S6EobCgpzWe0B38sVjMYDmSU+OEjHIH2qizOtxb5SXiKFVOC8i4KnoN3sRqDdMc0wWpDz8X6AY1O2L56sP2yEri/MLoE93kc1tR2CTZacLbHt6Z6M76wafchJXB/iroTL4L/AvxG2O/X7gnbZ4G/uQTKWZciHATtoIT1W2PvFR5rQZKvUbsrJQ4j1iqlDbH/G/H7rIz9D/moANcvi/j6a88XX2S/f58+lvCMGuVp2bLqtnr1rGZzwgRPs2ZVt/XoYbWs6UaCbdTImtd673n7bc/qq8e2bb211ZzWdnTdJk08jz9u1xg3zrP//qn7vzZu7Nl8c8/113umT7djrrnG06BB5uuss471Z/Xe8/PPnp497R689/zf/9XuHi64wPrUem8jJr/yivWn7d7ds88+tv7f/47+d0uhKHwoKc1nPID907wk+PmXJPv0xL4tH5jF+boG5zuiBO4tU4SjeY4IlgOC+78vi2N/xmpUPcmTsofA/wm+QwncJ+B3DMq6UfB4FFZLnO3xn2IJfPw0EWFf2sElcH+KuhM3YX0Zz8E+xCeOZv1+EFGXsy7Fwdjfcv8k29qD3x4bxCxVn0hF8ojvDgL2Php+6dkU6yO9GPs9P7wEylv02Gor++B0yCG5HbfNNtaM9rXXrFntq69a0rZsmZ3vwAOTH3fzzZaYpqpxPPVUO/6//7UEt5D3vvnmni+/tOuNHev51788a6xhSevee3teesmaIHtvNbyPPGIDQXlvfVTbtUt97gEDrKZ3yhRLpONrZMeMsdrfmpZ7t92sDG++6TntNHuufv019iF4+XI7f6GfP4WiNEJJab5iDeyf4WTsA8lMrA9Xbc7ZNDjXaSVwf5liB2K1hx6rJViGfZDIdOxLwTETSd5HqF9wrnxNvVLbOIuqteB3gp+a5bFtg2P/DJbhFC8XUnXAJ4UiH/Ef7Pfs8yASt9+CvVdFXc5iRHesBUuybgT5jOuxAdrqpdnnQcp7Duoo4l7wv8U9vhSreW5O7D35eqxZetRljSSef94zdarVCOZ67CGHxD54jR/vufVWz4UXeg44IHVtZvfuqfukNmtmZXn11eLdv3OW5I0YEbuXMLH+9VerFd1556o1wgceaEnqSy8lP2fz5pZ4/v578ul1jj7azr/RRrmXt3Fjq3X95puqtbXOWT/bY4+1xD++hlmhqNuhpDRf8RT4OVjTztHYP8gr83DehZRW09VUcUZwz+3BL8VqNz02Am+mY68K9j01zT73Y6NZvh88v/tEeK9PYKPjho/DJtnZjJIczh25Zdx9H47NZzimBF5HRd2KcCAxD/7yJNvDL5HSzZdcV+KJ4F6fIH3CGEbrLPdLjI/Bv5Nhn6ODsqxSAs9LucQ7VK3V3zp4Dg/GvgR4sgTKGFm0a2e1ahddVPNz7LijTdGSS5PaW2+1+VBXWaXq+hNOsA9xG28czfPRt68l2pdfbjXB6WoajzvOynrkkdW33X+/Jbabb5782GbNbGCpF1/MvYxnnGHX3Wqr6H9/FIrSCCWl+Yh1sX+O5wSP+2Oj7O6ch3NPAn97CdxjpngM/Njg56+JzUu6RRbHbg1+JPhWafbpjfUTegdr6jue6CZG/4Wq861uFtzrVlkceznW57YxNhXH29iXGVPBP1wCr6OibsWGxJLS7ZJs3zzYtnUBrt2gAOesafTDWrKMpGoLhXQxCfzdNbjnhWT+QjLsd3pICTw35RLjsW4x4eOm2HvpPOyL0MSR3Ssq9tvPPjStt15xr9u9uw3888sv1j8VrAZw0iTPW29F/7xkE855Xn/dM29e7B6aN7cmvt7bIErpjv/Pf2y/DTfM/pqrrWbXe/rp6O9foSidUFKaj3gS/HTwLQtw7q8pj2+Af8RqIMCSKx9E7wJca+fg3PtHcJ+DqV4DHE5VcHAWx3+ETWcQPu5NrCnv6SXwOirqVoT90sNmjonb2wfbj8/jNQdgydxiLLF7CvviJsrn4U5sdOv22KBkHvxuafYPm9l7svuyKYy1g2MyDWLksGbTd0b8vJRL1McSz4sS1r8VPN83lkAZI4177rEauyj6Hg4aZH1QJ070nHOONSP2PnXtYilGt26e2bNt0KdXXrEmu8uWWcKZ6dhmzay/6ZtvVl3vnPVrTRydd401rDnw1KmZ54hVKCorlJTWNppjzUoTh//PV7yO9T0qRHKXr2iBfTAIJysPm/IuJ3kf0dqGA/8NsalnihkXYx+O2satC/v+npHh2CbYB/XE5tj/DI7fpgReS0XdinpYQppuMKOp2PRG+bhe+IXRfKw28n6sJcDrET4HXbG/u+uDx42p2rIlWQwhlsz/RPbTi+wdHLdWFvs+B/77CJ+XcoruwfN6ZML6f2JfCHcsgTJGGpMmWc1eVNdfYw0rg/eeyZMzz31airHOOp4bb/SMHm0j+ebSrPb44+3e99zTpuM580zrL+q99dE95BBL3o8+2r48mDjR079/9PesUJRWKCmtbYSjpm5WoPMPBj8DG+Chbwncb7IIp5sI5xcNP5j+VsBrHhJcY9ss9m0K/gSslrJXLa/7I/jXkqyfSeZv6zdNeJ7iY/0SeB0VdTOuxZKlVNvfAj88T9d6A/w48O3i1j0K/ocI7/967IuknnHrJpK+aW44D+bJwTKxhi5VnBns3zSLfU8N9q34hCqL2ITU7/eFHriq5GOttewD06GHRluOhg1zmyu0LkXjxjZ1TvwH2LfeslGAP/us6vrvvos1E1YoFPGhpLS28QhW01CTATGyjbXBT8P6Usb309oGayoX5f0PAf889oGhS7CuR/C4kFNNNAQ/AZuDsX2a/YZi/Xs9yb9pzyXWSnOO0eCfSbLeBc8HxD6wJk7LoVBEGdeDn5uH83Qn+dyRV2D9LF0E97ZZUKbEuVg/IP1gRCdgf6ttsWmtFpLdQGb3kv2XcRsE19gj4tc/H9ETG1egUO9tBwbPVb8SuNeSi1NOsQ9MXbpEX5ZKjnXX9fzjHzawUuJrscMOnn339fTuHX05FYrSDSWltYnG2Kh/txXhWuHcd6vHrZuBjdoa1f1fF5RpPjY8f/y2WdgHtEJef1es6fQvpJ739e6gLMOwATGuqcX1wnnyks2X+go2b178uvpYLZHHBjh6A+sjHNXrpVAki6Ow39HutTxP+KVLr4T14Qi/xa4RbIMliD9QvT/t/SSfRzqMa7D3C7AvlRaT/H2+HVUHaBuO1TxnU76G2PvX/yJ+/fMRdwWv8dFx6xqQv1rMXGqgKy7eeMOanEZdDoVCoahdpExK6yEZbQO0BJ4uwrVGB8sBwbIz0A4YWIRrJ1MPOBR4FugCnJGwfWfgvAKX4XlgGFAf+AB7PhINBT4Mtn8PrF6L6/0NeA+YnmTbJKBr3GMH3A3sC7wLnAZsHZRDpJSMCZZr1vI8hwLvAL8krP81WPas5fmz0Qk4FbgAe1/uDBwAzE/YbzzQDWiY4jw9iZX7N+A24DBglYT9ngceiHu8KvBjlmVdCnwKbJjl/qWqK3BQ8POeceufAKYB1wJ9Uxx7QrDPRdj/tnOw5/3khP16BvstzEeB65JmzWDYMHjttahLIiJSOFHXkpZDTend4GdTmMF8EqMp1gwtHJxjK2JNUltHcO+DgmsfUAKvw8Ykbwa3UvCchQMw3U/N+7n2Dq5xXIrtYS1q/bjHPu7a+2H92LYsgedLoYiP1tjv6ik1OPZEbOTesG99shGow2bvfyvgPTisWX04FZXHaiH/mWL/Q4J9Us0TOhL8S3GPO2EtQuKnJGmB/c3PDq7fJjjnSTmU+1qsRjaKps35iquwPrsPYwNDtSE2IvlorJZ5Cfgjkhz7PdYff3nc6zYFGxwrvinwa+BHlMC9llxsv719WNpmm+jLolAoFLUL1ZTWVANgN+AF7BvvQluI1UCENaVrxG1buwjXT7RJsCyFmr/PgEXAxgnr18VqdD8NHn8LdAda1OAaYQ3J1ym2T8JqbDsHj3cG3gYuDh4/itXMvF2Da4sU0hzs9zfXmtIWwP+wmrAngD+Bp5Ls91uw7Fmz4mXlbOB24EugP9ZSoRlwY4r9xwfL3im29yRWUwowFbgBq3XtEazbAPubbw2shtWSQvY1pWDvJy3SlKPUtQGOxt7f/ofVPO8CHIv9X9wWe77eAu4AroC/PlwMBPoBpwfLE7GWLFsDrYLHocTXQwKbbQZLl8KHH0ZdEhGRglFSmkF9rMnqrUW85rfEmp+uQawp08AiliE0DPuwOSGCaydaAnwObJSwfmiwDJPS74Jl/xpcI/wgmup+JwXLrtgfz+rAqBpcRyQK31D1i65shEnYydh74VFUbyYLMDeIHkm25cuOwMfAlsAPWeyfLiltgXUF+CVh/V3Y3/bOweP4L8HWxxJTgJ+yuH4o7JaxVg7HlJKTsefrcuw9+DesKe/fsS8opmAJ/c7YFwSnEmuauy+wDGtmPRb7cuN77HfxCeB4LOkF+91RUprEppvCZ5/BQjVsFpG6q1ZJqXPuROfcGOfcN865R5xzTZxzvZ1zI5xzY51zjznnGuWrsFFYjH0zP7yI1/wW+0a5PvYB8lOsn82gIpYhtAmlUUsa+ghYB2gSt24o9iFnbvD422A5gNx1B1YQSz4TxSelPYGmcdcTKXVjsL8Ll8MxYVL6OnAZ8EiafX+lcDWlDYHBWN/xbE3CvsxKlpSG5UxMgn7CakHDpHQT4CtgHpaUrgosB8blUI4x2PtKOSalw7BazvuxRBIswdwGSyZvjtt3OfAv4Dms3+jKwD5YDerMJOe+EFgJ+A/QEXs/VVKaoFkzWG89eO+9qEsiIlJQNU5KnXNdgX8D63rv18RyqP2wL1Ov8d73BWYDh+ejoJXkWyzp6oMlpWOw5mrFrintgw1uVEoNhj4CGmFNdkNDidWSgn1YXEzNBjvqjn3jvyTF9vikNEx6lZRKufgGa+6aSzPSsGZwbBb7pkpKzwU2y+GayQwEGlP1bz2TFVitXni/jbH3NEidlAK8BGyBDXC3AfbF3GfYe81qWO1qLt05FmDvS+WWlLbHvoT4GTgubn046N/XJP/S8iTsffoZrEvEYynO/w3wEJaUPh6sU1KaYIMNoGFDeP/9qEsiIlJQtW2+2wBo6pxrgH3W+R1rWfVksP0+YPdaXqPihEnO1lg/pjHYN/VrYE94sZRSf9JQWGMdNuHtjvXvHBG3z3KstqOmNaXpmirPwBLWrsSaQdb5pLR3b7j7bvjyS1iwAB59FHr1irpUUL8+tG4NK60UdUnKRk1G4F0VS+wWZbHvb1RPStfFRslNHLk7V+sHyxFp96puPPYFG1hN7xjsn1W6pPRF7IvBE7Fmqx9iyfBALLHMpT9p6GvKLym9HWvivA/Wlzj0EfAy9mVDMuOwvqdDsffLZ9Jc4+/A+cTe05WUJthsM1i+HD76KOqSiIgUVI2TUu/9JOAq7HPI71jryZHAHO/9smC3iVSdQeMvzrmjnHOfO+c+r2kZ6qrvg+XewTKsKW2CNestlmHALEor6ZqJPT9hP6/wg2pi7Ul8v9xcZEpKPTCZWE3pROCPGlynbHTvDu+8A3vtBZMmwUMPwS67wHffwbHHRleuW2+FZctg9myYMgVWWy3zMfLX33Iu/UpXJfv+k79iTTpbxq07PlhuhjXPrKmhWN/FXPu3j8NqSusB+2Nf9G2PJaWLg3Mm+gD7uw77RX6EJcONsOcul/6koa+xWtbGNTg2CisDewBXYl+KxlsB7IRNFZbKxdj748vYIFupLMW+tFgPe75TDTJXsTbdFEaNgnnzoi6JiEhB1ab5bhtsYNreWIuo5tj/+qx472/33q/rvV83896VZR72wWvT4HFYUwrFbcI7DPsw5ot4zWwMJ/at+lCsBifxQ9N3WO1Irh8AMyWlEJurdACllbDnXceO8OabVhu5+eaw005w5JGw6qrWv+naa2FATeqja2m33eDoo+Hhh+Gkk2xUyssuK345ytCfWNPTXGpKVyP7msGwlisc7CjsUzgK+1Jtixyum2h9cq8lBasp7YANktQpWPc3LCmdQPL3t6VYH9qVsG9dJ1L1i6+aJqX1qVkLjijsESwfruHx84EhwMFZ7v8VVrtaav9vItWokTXfVX9SEakAtWm+uzUw3ns/3Xu/FOtmsjHQOmjOCzY7RqoxYySNb7EPMNOwJqM/YN/qDyrS9XtgtbKl2IvlI6yv0+XYB55RVO/fFT5/udSftcaa6mWTlHbDamLrdFJ6993QtSvsuKN9Ux+aPBkOOgj++ANuvx1cLsPm1FKrVnDzzdaU+JBD4Jpr4PLLYY89bHL52urWDT79FB5JN5xPectlBN62WPPNXGpKIdY09lisy8GBWJKS9beWCVpjo2nn0p80FI7A+5+gDA9g05n0J31T0ZeCZdhocjKxf2Y1ab5bbiPw/g37QvT7TDumMR37klVqaP31oUkT9ScVkYpQm6T0N2AD51wz55wDtsI+o7+D/T8DOAQbiE9yFCY7YR+wZdiHyWLVlB6CNdF6PNOOEQgHXjoN+3B4QpJ9wmlhcmnC2z1YZpOUroYlsGMy7Fu21lrLakYvuQSGJxl7esYMOPlk2Hhjqz0tlquvhk6d4PDDrfkuWGI6cSJcdVXtEuRBg+CTT2yky/32gzVzndGzPIzBErL6WewbjrybbVIaP1dpY+AY4Hns7/EdYIfsi1lF2JympjWlYH3kX8AGOmiBfcGXLil9GatZfjVuXXj9mtSUjsWm9yqHpLQD1lIn2Xy0dVb9+tY6pJRsFgwPpvlJRaQSeO9rHFhXkHDKsQewzyF9sC+0x2LTkDXO4jxeUTWOAO/B3xC37i7wU1PsfxH46/J0bQd+HPjXS+B5SBW7g18zzfbG4JeBPz+Hc+4YPOcbZNjv5GA/D36jEnguChIPPOD54w9P69bp93vzTc/cuZ4hQwpfpksusTeMSy6pvu3gg23bfvvV7Nx9+9p9/PqrZ9gwz7x5nnvvLe5zvuOOnl69Cn6dg7Df3X553hfsvWMR+EvBnxocu2mw7R/B41VqUOazgmNb1eDY9sT+XvcAXx/89ODxuRmOXSnh8aHgfwzOUZPn/nPwr2bYpz72XlTU372EODJ4ftaKuBxFiT339IwY4VmwwN5Dtt8++jKFMWaMZ/jw6MuhUCgU+YvPU+aDtUlK8xUl8ASVXGyMfSg4Jm5d+KGue8K+/bEEbD74hnm49hbBdfYrgeehNvEt+Ody2P/o4L67ZthvP2IfctuUwH0CnoYNPbvs4unWLbaua1fPzjt7zjnHc801lmxlc64ePTxLl3quuirzvl27esaN88yY4VlrrcLcm3OeG26wN4ubb7bHifvUq+f54gvP+PGexo1zO3+DBp5PPvHMnOnp3t3WXXutZ8kSu79Cv3Z9+1py771n/nzPCSfY/RToeoOx3929gsdtE7Y3whI5wF+Avbfk8r7yE/i3wf9B1b+/PsF1/1mDMj+H/T3X9J7/CKJJ8Pj2oCwHF/q1TYh7wE8Dfzz4M8G3S7LPmUHZtixy2eLjNSz5jur6RYv99vMsW+b5+mt7v/vxR8/339v7adRlGzbM3hMOOST6sigUCkX+QklpuUV9rHYgPulZj9i3/fH7PkEsSRqah2s/AH42sQ9w5Rp3k7pmOVlcBH4p+HoZ9hsWPNeTS+Ae/4p77on9Qf30k2fatKp/ZAsX2vLHHz1rr53+XGFCFp/gpotevTy//eaZOtXTv3/t76VRo6qPzznHyn7FFemP23JL2+/kk3O73nnn2XF77x1b17OnfVi9/PLCvWZt2tj5Fy70zJ7tOf54zwsvWFlee82S5QJctwn45Vgt4dZY0rlD3PaLwc/EktVHwI/N8fxvYn8fi8H3Tdj2A/iXalDmKeDvrcU9vwb+prjHmwVlHFKo1zZFHEPsvdqDfyVhezfwfwbbTi1y2TqAPxH8reCXgL+kyNcveuy9t/2Nv/OOp1kzW7fjjvb3d8IJ0ZfvwQftfaFp0+jLolAoFPkLJaV1IRpT/cPCOtgHmJuD5Sm1vMZK4BdQ9QNcucZRwXPSO8v97wP/axb7hTU+b5bAPQKWzHjvufpq+/nppz133uk57jjPRht5WrSwD13/93+eyZOtRrF+/eTn6tXLauvuuSe3Mqy6qp170iSr+cv1Hpo3t1qLZ5/1LFrk+fxzSwy32cazfLnn/vuzO8+LL9oHubZts9t/3XWtVjjZ+R95xDNnjj1/+X7NDjzQM2uW3du993o6d45tO/ZYez3/97+C/c78iP3+TqL6+8YzwbqrsOamiYlTprgrOP6KJNuuxVp05PKFV4/gfMfW8p5dwuPOBXpu00U9rGVL2+B+Eu/rUez9dxr2hUC+rtuMWO13sugHfnxQnmng36NmzazLJsL3ufffjyWkYbz8sr2HtG8fXfnatbP3weuui/65UigUivyGktK6Ep9Tta/nK1j/qJZYLUS65qobgr+N9H0m/w/7YLJeCdxrbWPt4F72Dx5vgdUAp2qe+zb4D7I4b+PgvNeXwD2y5Zb2bf9TTyVv1poYe+2Vvibg5ZetL2m2taTxMWCA1dD+9ptn4MDM+6+6quewwzyPP24fEL33TJzoufVW+1A4fbrF6NHVPzimK8OyZdkldA0ber76yjNhgmellapvHzrUynTssfl9zc4+28773nupmzxfe63ts//+Bfm9eZpYbeYCqvZd/yLYtijYlmtf9cPAf0/1/piA3z4497Y5nG/v4Jh1CvA8RB0vY0n6IeDPCe7z3OD1+T6P13kA/O/gWyfZtjH4GcH2dUvgOSlKvPKKvc8la57fv7+1FLnlluKVp3lzz4knej791Loo3Hij/f0PGBD9c6VQKBT5DSWldSVuBT8r+Lkv9iHm9ODxnVizu8QagTDuCfb3WPLVOsk+d2FJbqpzlFPUBz+P2IfqB4N73zfF/j+BfzjLcx+M1ZhGeo/16nm+/db6QDVvnv1xL75oA/kkJp777Wd/kP/8Z83LtPba1jfTe+undcMNnssu85xxhmfjja1p7n772QAe4RvA77/bfptsEkus+/b1fPON1VSuumpuZbjtNs/ixZ5VVkm/35ln2vV32SX1Pp99ZuXIx+vVoIHnjjvsmvfem77fWoMGlrTOn1+QD6b/JVZD+iVVv8yaAf558AuDfWrSBzRVNMES3f/lcMyVQVny0V++1GJlYoMueewLgSZYgrocfPM8XKMh+LnB+W9M2PZPrPXN9+B7lcDzUZTYd1/7G/zXv1Lvc8019uVWpq4O+YjttrM++d57Ro6MfUH3wQfRP1cKhUKR/1BSWlficOzDxSrgzw5+Dmv+Dgker5Hi2JHg3wV/WrDfgUn2+Qn7lj7q+8xXvA1+BPZB74/gvpM1K3TYB9/LS6DMWcdBB9kf0J575nZcz572wWfKFPvwtffentNOsz6hH39c+0F2OnTw/OMfnnfftRrPcFRL760GIkxYjzrK069f6vM0alSzJnSdO1vS/fjjqffp18+axz36aPpzHXqolXezzWr3nLRoYbUz3nsuuCC7Yzp1stfom2/y3q+sJzaKdD0sIf0yWN8c+xv5D5YMevDb5fG6YK07vkuyfj2sJjVx/fvgh+e5DKUUbcEPpOqAUzsHz30+RvfeMjjXKCzRXQcbLO/RYP1z1GxU47KMFi3sS7ARI9K/z7Vuba003n67sOXp3Nmu8/XX1jIjLOO+++b+ZZxCoVCURygprSsxEPsgsR/4MVjfn3Bb2NfxmCTH1cea410OvkHwc2Jy1iU4/oQSuM98xSVYE8V9g3ubD/6tJPt1JP+1QgWNBg1sQKNRo7JrtpsYm2ziefJJS8zCP8Sff/assUZhytuqlSXPYRJckzLnEuHgSBtuWH1bkyZWAzpzpqdjx/TnadLEajHSJbiZomVLqwFZutRz+OG5HbvVVtbv9I47CvZc3UCs9cXqxN5fWmI1qfmuofx3cI1ecesaY4MZeezLszARro/9zV5TyN+VEoyuwXNxXB7OdQ1WO90ZG5xtEvaeuBj8GdSNVjFZR/hF3iabZN736KNt3732Klx5XnrJvrTLxwBxCoVCUR6hpLSko0cPz/PPe668MuO+DbAPGG+QPAGdBP6hJMeFHzbD2tGRVJ8vL0zcij0iZSFj1+CefsCayd2B9StN3C8cMGq3EihzVnHYYZmbnmYTrVt7Bg2yxCnqe8pnNGtmgy598UX15q/33mvP3c47Z3euK66whLKm08M89JAdv+OONTv+oousvM88Y02f81xregr2u98SSwY9hZ1/dzWqv3eF82JehrXWWI4NvjMwWL9/Hq9fLjEV605R2/OMBf9i8POe2Oi+N2Ej/UZ9j0WPF1/0/PJLdvvWq+f58ktrxvviizZw3DXX2Gi9P/1krTG++MIS3ZpMIXPMMfZ3fdxx0T8vCoVCUbxQUlqycdBBnrlz7Yn488+s5lgcjn1QW0r1ERUfBT8hyfow4QwnQ78bG9gifp8bsb5HNZ0YvhSjU3DfHhvk6Yjg58T+oLtTRgm5c56xY21QjKjLUsqx++72wXH5chuR+Mwz7UOl955zz83+PL172wfTyy7LvQxh89+zzqr5fdSvb1PHTJpk5/rww7zOY7oP9ru/BrHksNAJy8/E+rE6rE/j58Hj9ljt6N1x5Ym8/3YE8SrWxxRsoKJnanCO/sHzd3QJ3E/k0aaNdR/IZZqnzp3tS6EJE2L/o4cP9zz8sL2XfP21rf/hh9y6GnToYP3lX3st+udFoVAoihtKSksy+vSxJ+C992Lfmm63XcbjrsM+aLycZFuYdHmsxmHtYP0l2IAWYVO844N9OsYdO5rcp38ohxgX3OvW4AcHP++dsE/4fHQogfJmjM02s9+VAw6IviylHm3bei680Ppmhm84Tz+de/PhRx+1D5HJRukFO99OO1nie8cd9qH1jjvsQ+zbb+cniXQu1qQw3SAtOcYG2O/+jmQ/V29t40ZsELLuxFozxA9Adi32fvU61sIh8t+jCOJSrInt/sS+hMx14KNTg2NTjTheUXH44fa3M2RI7sfWq2d98ZP9He+6q801/OabqafaSoxbb7UEebXVon9eFAqForihpLQk47zzrBana1fruzZ/vo1CmuG4cNqW/0uyzYEfBv4k8HOw2gaw5ltfxe23BbFEDWyQDY/1MYr8eclz3If1V6uPJeWLsWaC8fs8iyWvUZc1q7j7bqtd16TquUWTJp7u3Wt27ODB9mZ12mnVt+2yi00tE76h/f671ZxMnGjN+7p0ye99vPyy1QDX9F4SYmXsb/8Y8Pdj81UW+rXYAPs7XIK12BhP1RYa3YJtHvxLUf/eRBRhDfZ8bFR1D36bHM/xHrHa1oqP11/3/PhjYc4dtojIpjXF2mtnP22VQqFQ1L1QUlqSMXas5403Yo+fe84zfnzG45qCPxF8owz73YsNYNIQ/G9YE7BwW5iEnhw8DmsrNo76OSlAtKHqoCqJc702wmptbiqBsmaMZs1sfr0774y+LJUWr73mmTw51sTeOZvc3ntLQgvQ3zNp9OhhSelLL+XlfA5LEC/FRud+t0jPZ3es1cd8bFTxxO13Yu9J50X9ukcUqxKrId04WF6Uw/FtwS8Df0EJ3Evk0bGjJYL//W/hrnHLLfZekGk09LfeshF3W7eO/nlRKBSK4oeS0pKLjTaymz/44Ni6I4+0dXmal3BH7EPNgcHy1ITtE7CaEbARGheSOdGtC3EbVvMQPt4qeH52KoGyZYxw9Mhhw6IvS6XFllvac3/rrTZ9w9132+Orr86+2V6+4l//smsfdVRezjcWGyBtPLH3hKijL/hfwa9bAmWJIhw2QNvZwePh4D/M4fjwfX+9EriXyOM//7G/lzXXLNw1GjWyKbX++CP1aLonn2zlOPro6J8ThUKhiCaUlJZc3HKL9Tdr0SK2rkuX1E0EaxANsZFmf8Q+nCTON/gSNj/hSliNal2anzRdHBU8H72Cx1djCXmzEihbxnjjDZu6pdBTqiiSx6OPVn3zOuecaMrhnOfVV63Jfx6mk3gb/MfkXhunKF6EfUybZrn/I9gUMBU15Uuy6N3b/k6ee67w1+ra1eZ7/vbbqv/bwcYCWLq0dtNLKRQKRfmHktKSisaNPbNmeR54oPq2L77wvP9+3q51D5aAeWyeuvht4RyeFwTbCz7ybMeONu9ixM9/OP3L34LH31ImAzztsYf1QT7vvOjLUsnRvr3Ntbr99tGWo3Nnz7RpNm3FWWfZF12bblqjc92DzV3ssRFvI3+OFdVi++D12TKLfRtgX0jeUQLljjxee8364Nd0SqdcY/PNLfkcN86me1lzTZvCa8qU5MmqQqFQVFYoKS2p+Nvf7Ma33bb6tgsvtL4vuQwvnyZ2wD7ITEuyLZwmZjH45wt5v40bW/OpcOqbf/wj0ue/ETbw0VjwGwbPwb+j/p1IF61aee67z567zz+36QSiLpOiNGLnnT2LF9vvxoIF1tc0WfP/Bg3SjgAcfjHlqd6iQlEa0RKryc6mj+jmwWu5WwmUO9I4+OBo/udsvbVN3RT/QWfCBM/qq0f/nCgUCkW0oaS0pOLNN21Ao2QfEgcNsifl8MPzcq2GWNPcN5NsC+ew81jtYUHutWtXq8nx3vPsszZy6NKlyRPyIsZ62OBGC4L77xv170SqOPhgaw62dKnnggssuYi6TIrSilatbGThLl1s5N8ff6w6iMp223lmzrRa9unTPVdeWe0chxN7L+gf9f0oUsYIbETdTPuVVZeEQsVBB9m0K++/H113hw039Bx4oGfVVaN/PhQKhaI0QklpycRqq9lNn3FG6n3GjvW88krerrkVNj9n4vr64P/ApospyL2uuaZ9Ozx3rmfHHW1d8+aeUaNszsdNNon0tdgKqyX+KerfiTBatvQccYSNzvj999Y003ubrH3w4OjLpyj92Hhj+yD++efWdPCUU6zlxahRnvPP97z4ov1OJTSj35pYUprrXJiK4sUVWDPrJnHrdsGa6b6CjRNwIDaOQKVOpYNzNl+w9/ZemmpuYYVCoVBEEUpKSyauvtqa23XsmHqfyy6zD5Zt2hS8PEOwqQPyds4GDWzOtpdesvucONHmZYvfp1s3z08/Wc3NFVd41lvP83//5znpJBuBeLfdbCTDIrwe61OEvrSZont3z/XX22Ac3nu++cbzyCOe226z50WDGilyif32s7+v8A322WftyyCwpvQ//eT57jtPw4Z/HbMalpDOiLrsdSHq17cuGuee67nrLvuiqVmzvJx7y+B12jN43BhrCTMb/Kfgfyb25cKxUT8PUUSvXp6337bf+wcfLNr/EYVCoVBkHUpKix716lkT1fhvaZs0sWZ0jzyS/th117Un5pBDor+PXOOee6zs48ZZM8FUg0s0bx6b1y1Z/PyzZ999Sy8hq1/fpuOYMcPz73/X/nznnWdfQCxebB9g1103+ntU1I3o29ezzTbVuwlsv739jcWN8t0ES2RGRl3muhCXXGLPb9hc2nvP7NmeE06o9bnrgf8d/BPB4z2D122b4LHD+pNeiPVBjfy5KGbsuKP1qZ4zx/P3v0dfHoVCoVAkCyWlRQ3nPLffbjf3xx+ea6+1PqJhErbZZpnP8csvnhdeiP5ecokLLrD7y2V02A028Oy+u6dfP0vgu3a1DxdhP9TRo61vUCn0pezQIVauP/+0fsG1SZovvzz2jX63btHfn6Jy4umn7Xc4rjXGVPDPRF2uco/Bg63/9z33WK002JzUr7xif+thN4ZaxLVYf9GVsGm8JmPJauT3HmXssot9sffZZ9byJOryKBQKhSJVKCktatxwg93Y9dd77r/fasLCmx0xIrtzXHWV/ZMt5f4w9erZoE0TJthUNt577rwzf+c+8EDP11/beb/7zjNkSLT3G07Avtdenn32sZ9zGbCpUSP7cuKss2LzXd5wQ/Svo6LyYvBg+/075pi/1u0PfqPanHPttT2nn26/2/fem3a03zoZDRrY++DkyVUHmgJLUEeNshYWtfwCaihWO3oi1if+qqjvuxix3XaeTz+1Lym//trzzjvW4ujuu2P/Yz/5xAb9irqsCoVCoUgXSkqLFqefbjd1xRWxdW3a2Le3K62Ufc3aBhvYeQ48MPp7ShUHHWRlfP55qwm48cb812g659l1V0t8lyyxxDCqD7sjRsS+VGjUyJrmZTsReps2nnffjf3SL1xY9XdEoSh2fPWV5+OP83Ouk06yGkLvPZMm2fKAA6K9vzZtbHCn556z+SELPb/vqafafe+2W/Ltq65qLWc++KDW3RJ+JjZy+KCof48KHR062Hvtzz97nnzS4r33PD/8YC2KfvnFXuNS/gJXoVAoFGEoKS1KrLuufTB7+OHan8s5S8SeeSb6+0oWjRpZv9HPPy9Ov882bTyPPWa/MO+8U/wmWt2727Xj+uH9VZudad7Q7t09Y8Z4Fi3y7L9/lQFmFIrI4sQT7Xe6X7+an6NhQ2sK7L0lC+3b2/vBF1/YKOJR/a4fcIBNpbRsmdWsjR5t7839+xfmes2bWy3oiy+m3++ww+y5qmUz3ouwhPTrqH+HihGPPGLvncnm31UoFApFuYWS0oJHs2Y2jcevv+avCdG111qNWosW0d9fYvzzn/bibbNNca978MFW2zB7tk1/UazrHn+83e8qq8TW9e9v6045JfVxzZvbB+LZsz2bbhr966ZQhNGxoyVql1xS83Ncc439DZx0UtX14WBKcc2DCx716nn23tszcqRd++OPYyN/t29vA+BkShoTo2XL7PY74QS75tCh6fdr0MC+bHzjjVrd6wAsKT016t+hQsfuu9vzeuaZ0ZdFoVAoFPkIJaUFjaFDbVCi5cs9m2+ev/Nusok9QfvuG/09xkfLllYL8dZb0Vy/Tx9rujVxoqddu+Jc8/33bZCjZOunTvX07p38uCeesNqarbeO/nVTKBLj+eft76gmTeLDhOHaa5Nvf/9961/ZtGnh76NnT+tz6L19OXjoodXv6ZRTsvsibcAAm9Jq6lTb//bb07cGadTInsNs3w/DvulrrVWrex4EvkHUvz+pIh9dLDp08EyZYrXupTDQnUKhUCjyEUpKCxKrr279sry3OSZPPTW/569Xzz7UPfFE9PcaHzfdZAl4lNOXDBxotcipRiju2tWaFj/2WObmtZmiUye733PPrb6tXz9rtvfDD1YbE67v2TM24FViLZJCUSqx1172O7rLLrkd16uX1f6PGJG6ie5GG9m5r7uusPew3XY21dbs2dY8PlVC1KiR9Uv89tvUrVk6drQ+itOm2aBtd95p93DPPanPe8QRts9WW2VX3jZtbOTju+6K/vWvTRx6qOfii21e7Xvusd+FadOsqa339jw+/7xNz5L43DVtaoPEXXGFJZ1jxlh3iGHDYvs8/7y9x6+xRvT3qlAoFIp8hZLSWkWyb8m32so+BP3+u/UTyraZV65x442W8OZp8vVax6ab2ot29dXRl+Uf/7CynH561fU9e9qHzz/+sA9IU6d6dtqp5tc5+mi7TqoPRxtu6FmwwBLTV1+NTRvjvee226J/nhSKVNGwof3e/vCDJW3ZHvfmm9Yctlev9Pv973/2d7DDDoUp/9pr2wBoo0ZZC4pM+2+1lfUDHzGiemLauLFn+HBLGAcPjq0/5xy7h99+s6b4zz4bu+8117Ra0mxHVQ/jppvsvaljx+h/B2oSZ55pz8mSJZY4TppkvxO33uq59FIbYOrBB+33ynt7fY45xuaufvvtWOK6aJHVML/ySmzdRx9Zgup9fuaCVigUCkUphZLSWsV333meespGwt1jD89DD9k/46++8vToUdhrb765PUl/+1v0z0PTpp4ff7QBTIrRJC+beOQRe37+8x97vN121q935kzPOutYU7xRoyyx79SpZtd4/XVrEphunx13tJrZ4cPtA9Z//pO6Sa9CUUqx7bb2N5Rtv72//932P+qozPs2bmxf0kydaqPP5rPc9evb39zvv1eZbzVj7LyzJaaffFJ1xNaHHrL72mOP6sccfrhtf+op+zJyzhzPf//rmTfPErJBg3Ir+6qrWn/eW26J/vXPNQ480J6n++7Lbv+997Y5nb23BPazzyw53Xbbql+2Nm9uv1MTJti+r75anEH0FAqFQlHMUFJa42jWzOYbnTgxVuBp06xpZqFqR+Ojfn370DNvnufmm6NtynTuuXb/+ew3W9to0CD2YXL0aFv+9JM17w33WWUV+wCYqu9bumjb1o69+OLo71WhKFQ88YR9cdOzZ/r9Onf2zJpl0xtlmzAMGGAtCby3Y194wfqj1rafYNg3c889cz92110tMf34Y0tMzz47eauLZNGzp3355L3VkHbpUrPyh7WBG2wQ/eufbWy3nT1vb72V28jKjRrZ+3D9+pn3bdLEs99+uX3RoFAoFIpyCSWltQ7nPOuvb81Xs/nHms/o39/6Hy1caE/Y22/bh7pilqFlS/tA+fTT0b8WiVGvnjWHmz7dRslN9mHpjjuseVjXrrmd+9BD7TkfMiT6+1QoChXdulmz1QceSL/fQw/Z+1CutZ79+llz+1tuidWE/fabNX+tSXlXXdXKUZv+9rvuai1ewiam996b/bENGljriCZNan795s1jTYLLYSCfbbax53zkyPyNMK9QKBSKSgslpXUi2ra1ESTHjbMn7sQTi3ftcGL4KAc3qk306GHf8N98c27HPf+8NT2LuvwKRaHj2mvtbyRVP8eBA+094KKLaned+vWtj/eECdb0Nn6apWzj5ZetCW1Nm+SHsdtulph+8EFufWrzFbvtZs9pskHUSim2285qu0eNsv9DUZdHoVAoFOUaSkrrVNSrF5tqZIstCn+9Jk3sw+Nrr0V/77WJG2+0D6Abbpjd/i1bWu1qKQzqpFAUOlZbzd6Qzzor+fYXXrDWEvmqJevf31o3jB9vNbXZHrfTTlbOE07ITzn69KldjWdt44EH7H7+8Y/ofweSxb/+ZV0YRo0q3hRcCoVCoairoaS0zkWLFp5vvrEPdfH9TNu29Wy5pTUNy9e1jjvOXqhNN43+vmsTHTpYU725c7Prx7XffnbfG20UfdkVimLEa69Zk9LELgrh9C7hgGL5iiFDrMZz7NjsEtNGjazP+LfflkeT12yiQQPPM8/Y8/uvf8Xuq2FDz2abRTNCb5s2NrjeE09YuZ55xv7nRP1cKRQKhaLcQ0lpnYy+fW2U2eXLra/nnXfGBhRZtMiauG23Xe2u0aGDjZz5wQfR328+oksXG0F47tzM/USfeMIGmdIIkIpKiV13tfeP+BFoe/SwkWp//70wU1Otv34sMV1zzVhC3Lq1zQUdJmmtWtl8mN5b/8aon6t8RqNGnpdesnubOtXz6KM2oJ73Ntrv0UcX9n2ofXv7X3HBBfZaL18eu/b55+s9UKFQKBT5CiWldTY6dLAPEjNn2uiZt9zi2WUXG9nx11/tCX733aqj0cbHyiunb7r27LM2uMWAAdHfa76ia1eb2H38+NQjPLZrZ/d93XXRl1ehKFbUq2d/G99/byOMP/ywNXlfssSmAinUdddbzxIg762p6Ny5sX8QM2fa1E/TplmydNll0T9PhYiwr+0TT1gLmEcftdYab71lz8PIkZ4DDvj/9u4/5sqyDOD490IQWERmOnTC1EjmlBkxbbJaIzMD/5DaWNNpairUhqykuaR/KFZbzRlbW+hykdYkRAt16SoUpv6hiBolYiiFLpQfUv7IOZGX7v6471cOr+95+aGe+z0P38927ZzzPM/77uJcu5+X6zn3c5+DW/V2oJg4Md8fvGHD3ve6pyd/T+iCBXk2SacX9TMMwzCaHjaljY/hw9/9KcawYfk+pa1b8ycRrZ8MjhiRv+bk7bfzdLj+pqhecUUuUCcXVOpUnHVWXtTl7rv739+7sFOTmnHDOJC4/PJ8QWvnzhyLFh3cPZ+HGiedlM85Cxfmi0Hz5iUuvTR/Orp9e2L16oP/PtCmxCWX5O/LTilfNJgw4b39vvnzc4O/e3di5cq8gN7Uqft+b6thGIZhvP9hU3pYx9ix+VPBnTsT06fn7+J77rn85i9dmlfz3bMn/0dwzJg8VWv27PwVEatWNXfq1ty5+T249tp9tw8Zkt+T1avr52gYhgH5PDx9emLbtnzRYNy4g/8dI0cmli/P573bbnPhIsMwDKPTYVN62MfHP57vj+x90x95ZO/KvaNG5e/57OnJ96Q++WQ+5v77E8cdVz/3DzJuvz035BdcsHdb7+qeM2fWz88wDKM1zjgjT3XeuDHfvnEwP7t4cT7fzZtX/99hGIZhHI5hU2qQG9Orrsr3VPa3f/z4xJIlecGRWbPq59uJGDkysWZN/lS4d3rzvfcmtmxpzuqehmE0K6ZMyRcQ77rrwH/m3HPzH9zrr6+fv2EYhnG4RtumNEpTWFXk6aFSHWPGwKOPwtixkBIMGwYLFsDChbUzk6T+zZsHN9wAF18MS5cOfOzo0bB+PbzxBkyeDG+91ZkcJUna1xMppTP722FTKgGMHw+zZkFPD7z+Otx0U36UpMFoyBB4+GE49VQ4/XTYtq3/40aPhmXL4LzzYMoUWLu2s3lKkrSXTakkSY0yYQKsWwebNsHcufDgg/vunzgR7rgjX3SbMwduvrlKmpIkFTalkiQ1zvnnw+LFcOKJ+TaE3bth1KjciI4eDS+/DDNnwkMP1c5UkiSbUkmSGmnECLjmmtyg7toFb74JmzfDs8/CihXw0ku1M5QkCWxKJUmSJEkVtW1Kh3Q6E0mSJEmSetmUSpIkSZKqsSmVJEmSJFVjUypJkiRJqsamVJIkSZJUjU2pJEmSJKkam1JJkiRJUjU2pZIkSZKkamxKJUmSJEnV2JRKkiRJkqqxKZUkSZIkVWNTKkmSJEmqxqZUkiRJklSNTakkSZIkqRqbUkmSJElSNTalkiRJkqRqbEolSZIkSdXYlEqSJEmSqhlaO4FiJ/BC7ST24xhynupu1rE5rGVzWMtmsI7NYS2bw1o2Q1PqeGK7HZFS6mQiXSsiHk8pnVk7D7031rE5rGVzWMtmsI7NYS2bw1o2w+FQR6fvSpIkSZKqsSmVJEmSJFVjU3rgflE7Ab0vrGNzWMvmsJbNYB2bw1o2h7VshsbX0XtKJUmSJEnV+EmpJEmSJKkam9L9iIhpEbExIjZFxHW189HBiYjnI+KpiFgXEY+XbUdHxMqIeK48frR2nnq3iFgSETsiYn3Ltn5rF9nPyjj9W0RMrpe5WrWp4/cj4sUyLtdFxPkt++aXOm6MiC/VyVr9iYhxEbE6IjZExNMR8a2y3XHZRQaoo+Oyy0TEiIh4LCL+Wmr5g7L95IhYU2p2e0QcWbYPL683lf0nVf0H6B0D1PKWiNjcMi4nle2NO7/alA4gIo4Afg5MB04DLoqI0+pmpUPw+ZTSpJaltK8DHkgpnQI8UF5r8LkFmNZnW7vaTQdOKTEbuLFDOWr/buHddQRYVMblpJTSfQDl/HohcHr5mcXlPKzBoQf4TkrpNOBsYE6pmeOyu7SrIzguu80u4JyU0ieBScC0iDgb+Am5lp8AXgGuLMdfCbxSti8qx2lwaFdLgGtbxuW6sq1x51eb0oF9GtiUUvpnSultYBkwo3JOeu9mALeW57cCX66XitpJKT0E/KfP5na1mwH8OmWPAkdFxPEdSVQDalPHdmYAy1JKu1JKm4FN5POwBoGU0taU0pPl+X+BZ4ATcFx2lQHq2I7jcpAqY+uN8nJYiQScA9xZtvcdk71j9U7gCxERnclWAxmglu007vxqUzqwE4B/tbzewsAnbg0+CfhzRDwREbPLtjEppa3l+TZgTJ3UdAja1c6x2n2uLlOOlrRMobeOXaJM+/sUsAbHZdfqU0dwXHadiDgiItYBO4CVwD+AV1NKPeWQ1nq9U8uy/zXgYx1NWG31rWVKqXdc/qiMy0URMbxsa9y4tClV0302pTSZPM1hTkR8rnVnystPuwR1F7J2Xe1GYDx5itJW4Iaq2eigRMQo4HfAt1NKr7fuc1x2j37q6LjsQimlPSmlScBY8ifYp9bNSIeqby0jYiIwn1zTs4Cjge/Wy/CDZVM6sBeBcS2vx5Zt6hIppRfL4w5gBfmEvb13ikN53FEvQx2kdrVzrHaRlNL28sf3f8DN7J0KaB0HuYgYRm5kbksp/b5sdlx2mf7q6LjsbimlV4HVwBTyVM6hZVdrvd6pZdn/EeDfnc1U+9NSy2llun1KKe0CfkWDx6VN6cDWAqeUVcyOJN/of0/lnHSAIuJDEfHh3ufAecB6cg0vK4ddBtxdJ0Mdgna1uwe4tKxGdzbwWst0Qg0yfe57+Qp5XEKu44VlhciTyQs4PNbp/NS/cu/ZL4FnUko/bdnluOwi7erouOw+EXFsRBxVno8Evki+R3g1MLMc1ndM9o7VmcCqMrtBlbWp5d9bLvgF+d7g1nHZqPPr0P0fcvhKKfVExNXAn4AjgCUppacrp6UDNwZYUe7hHwosTSn9MSLWAssj4krgBeCrFXNUGxHxW2AqcExEbAEWAD+m/9rdB5xPXoDjTeDrHU9Y/WpTx6llWfsEPA98AyCl9HRELAc2kFcInZNS2lMhbfXvM8DXgKfKfU8A38Nx2W3a1fEix2XXOR64tayGPARYnlL6Q0RsAJZFxA+Bv5AvQlAefxMRm8gL0F1YI2n1q10tV0XEsUAA64BvluMbd34NL5BIkiRJkmpx+q4kSZIkqRqbUkmSJElSNTalkiRJkqRqbEolSZIkSdXYlEqSJEmSqrEplSRJkiRVY1MqSZIkSarGplSSJEmSVM3/AQ0P3YeBgTwRAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 15,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2022-01-16T22:26:09.084Z",
          "iopub.execute_input": "2022-01-16T22:26:09.088Z",
          "iopub.status.idle": "2022-01-16T22:26:09.526Z",
          "shell.execute_reply": "2022-01-16T22:26:09.535Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}